Ampera Technologies

https://amperatech.ai

Founded :

2024

Type :

Services

Size :

20-100

Stage :

Profitable

About

At Ampera Technologies, we empower businesses with cutting-edge data analytics, quality assurance, and data engineering solutions

Company social profiles

N/A

Jobs at Ampera Technologies

Advanced Machine Learning Engineer

at Ampera Technologies

Posted by Faisal AshrafNomani

Remote only

5 - 10 yrs

Best in industry

Data Science

Machine Learning (ML)

Python

PyTorch

TensorFlow

+3 more

Role Summary

We are seeking an Advanced Machine Learning Engineer to design, build, train, deploy, and operate scalable machine learning systems. This hands-on ML engineering role focuses on implementing models and production systems—not solely conducting data analysis or research. It requires strong Python and deep-learning expertise, experience with frameworks such as PyTorch or TensorFlow, and the ability to own the end-to-end model lifecycle, from behavioral/event data preparation and feature engineering through model development, large-scale training, deployment, inference optimization, and monitoring. The engineer will work with Snowflake, Databricks, Spark, and GPU-based training infrastructure to deliver reliable, efficient, and maintainable ML capabilities.

Must have - Buulding and operated production ML systems using Python + PyTorch /TensorFlow + Snowflake/Databricks + MLOps.

Key Responsibilities

Machine Learning Engineering

Implement and optimize machine learning algorithms and models for business use cases, including classification, regression, clustering, and recommendation systems as applicable.
Translate model requirements into robust, reusable, and testable Python components.
Build model training, validation, scoring, and inference workflows that can be integrated into production applications.
Evaluate model performance against agreed technical and business metrics and implement improvements to accuracy, latency, robustness, and resource efficiency.
Apply appropriate validation, error analysis, and model testing practices to ensure reliable behavior.

Train models efficiently on GPU infrastructure using techniques such as mixed-precision training, gradient accumulation, checkpointing, memory optimization, and distributed training strategies where appropriate.

Use PyTorch, TensorFlow,Pyspark or equivalent frameworks to implement experiments and production-ready training code; apply rigorous evaluation, error analysis, and reproducibility practices.

Build model-training workflows covering event/token sequence preparation, tokenization, temporal and positional representations, self-supervised pretraining, and downstream fine-tuning when appropriate to the use case.

Design, implement, train, and optimize deep-learning models; develop custom architectures where required, including transformer- and attention-based models for sequential or behavioral data.

Deep Learning & Model Architecture

Data Platforms & ML Pipelines

Use Snowflake and SQL to access, query, and prepare enterprise data required by machine learning applications.
Use Databricks for scalable data processing, feature preparation, model development, and ML pipeline execution.
Develop and maintain reliable pipelines for data preparation, feature generation, training, batch scoring, and inference.
Use PySpark and distributed processing techniques where appropriate for large-scale workloads.
Work with data engineering teams to integrate upstream data sources and ensure dependable data contracts, quality checks, and pipeline operation.

Productionization & MLOps

Productionize ML solutions and integrate them with services, APIs, data products, or downstream business workflows.
Implement model and code versioning, experiment tracking, reproducible builds, automated testing, and release practices.
Monitor model outputs and pipeline health; troubleshoot failures, data changes, drift, and performance degradation.
Improve operational reliability, scalability, observability, and maintainability of ML systems.
Collaborate with platform, cloud, and DevOps teams on deployment automation and production support.

Build observable production ML systems with appropriate logging, alerting, performance tracking, and operational safeguards.

Optimize inference latency, throughput, memory use, and serving cost through model architecture choices, batching, and serving-side optimizations.

Software Engineering & Technical Ownership

Develop clean, modular, documented, and maintainable code following engineering standards and design principles.
Contribute to technical design, code reviews, testing strategies, and architectural decisions for ML systems.
Own assigned ML engineering deliverables from technical design through implementation, release, and ongoing support.
Identify performance bottlenecks and improve runtime, resource utilization, and processing efficiency.
Collaborate with cross-functional engineering and product teams to deliver dependable ML capabilities.

Required Skills & Qualifications

Strong proficiency in Python and software engineering practices, including object-oriented design, modular development, testing, debugging, and code quality.
Strong ML foundation and mathematical understanding
Strong understanding of the end-to-end ML lifecycle, including model architecture, training, evaluation, optimization, inference, production integration, and monitoring.
Strong SQL skills and experience working with structured data and data pipelines.
Hands-on experience with Snowflake for querying and preparing data for ML workflows.
Hands-on experience with Databricks for data processing and/or ML pipeline development.
Strong hands-on experience with PyTorch, TensorFlow, or a similar deep-learning framework; experience with scikit-learn is also useful where appropriate.
Understanding of production ML concepts, including reproducibility, model versioning, deployment, monitoring, and troubleshooting.
Ability to design reliable, scalable, and maintainable software components and pipelines.

Familiarity with GPU training concepts and distributed strategies such as Distributed Data Parallel (DDP), Fully Sharded Data Parallel (FSDP), or equivalents is an advantage for large-model workloads.

Understanding of model optimization, inference efficiency, and integration with production services or applications.

Experience developing and deploying ML solutions at scale, including end-to-end model lifecycle ownership.

Preferred Skills

PySpark, Apache Spark, and distributed processing for large-scale behavioral or event data.
MLflow or similar tooling for experiment tracking, model registry, reproducible training, and model lifecycle management.
Experience integrating Snowflake and Databricks within enterprise ML workflows.
Cloud experience with AWS, Microsoft Azure, or Google Cloud.
REST APIs, containers, CI/CD, automated testing, logging, and observability.
Experience with fraud detection, anomaly detection, behavioral modeling, identity or account-abuse signals, recommendation systems, or other large-scale ML applications.
Exposure to Generative AI, LLM integration, embeddings, or RAG as an additional advantage.

Education & Experience

Bachelor’s or Master’s degree in Computer Science, Software Engineering, Computer Engineering, or a related field, or equivalent practical experience.
At least 5 years of professional experience building, deploying, and supporting machine learning solutions at scale, or equivalent experience demonstrating the required engineering depth.
Demonstrated ability to take ownership of engineering deliverables and work through implementation, deployment, and operational issues.

Role Summary

Must have - Buulding and operated production ML systems using Python + PyTorch /TensorFlow + Snowflake/Databricks + MLOps.

Key Responsibilities

Machine Learning Engineering

Implement and optimize machine learning algorithms and models for business use cases, including classification, regression, clustering, and recommendation systems as applicable.
Translate model requirements into robust, reusable, and testable Python components.
Build model training, validation, scoring, and inference workflows that can be integrated into production applications.
Evaluate model performance against agreed technical and business metrics and implement improvements to accuracy, latency, robustness, and resource efficiency.
Apply appropriate validation, error analysis, and model testing practices to ensure reliable behavior.

Use PyTorch, TensorFlow,Pyspark or equivalent frameworks to implement experiments and production-ready training code; apply rigorous evaluation, error analysis, and reproducibility practices.

Design, implement, train, and optimize deep-learning models; develop custom architectures where required, including transformer- and attention-based models for sequential or behavioral data.

Deep Learning & Model Architecture

Data Platforms & ML Pipelines

Use Snowflake and SQL to access, query, and prepare enterprise data required by machine learning applications.
Use Databricks for scalable data processing, feature preparation, model development, and ML pipeline execution.
Develop and maintain reliable pipelines for data preparation, feature generation, training, batch scoring, and inference.
Use PySpark and distributed processing techniques where appropriate for large-scale workloads.
Work with data engineering teams to integrate upstream data sources and ensure dependable data contracts, quality checks, and pipeline operation.

Productionization & MLOps

Productionize ML solutions and integrate them with services, APIs, data products, or downstream business workflows.
Implement model and code versioning, experiment tracking, reproducible builds, automated testing, and release practices.
Monitor model outputs and pipeline health; troubleshoot failures, data changes, drift, and performance degradation.
Improve operational reliability, scalability, observability, and maintainability of ML systems.
Collaborate with platform, cloud, and DevOps teams on deployment automation and production support.

Build observable production ML systems with appropriate logging, alerting, performance tracking, and operational safeguards.

Optimize inference latency, throughput, memory use, and serving cost through model architecture choices, batching, and serving-side optimizations.

Software Engineering & Technical Ownership

Develop clean, modular, documented, and maintainable code following engineering standards and design principles.
Contribute to technical design, code reviews, testing strategies, and architectural decisions for ML systems.
Own assigned ML engineering deliverables from technical design through implementation, release, and ongoing support.
Identify performance bottlenecks and improve runtime, resource utilization, and processing efficiency.
Collaborate with cross-functional engineering and product teams to deliver dependable ML capabilities.

Required Skills & Qualifications

Strong proficiency in Python and software engineering practices, including object-oriented design, modular development, testing, debugging, and code quality.
Strong ML foundation and mathematical understanding
Strong understanding of the end-to-end ML lifecycle, including model architecture, training, evaluation, optimization, inference, production integration, and monitoring.
Strong SQL skills and experience working with structured data and data pipelines.
Hands-on experience with Snowflake for querying and preparing data for ML workflows.
Hands-on experience with Databricks for data processing and/or ML pipeline development.
Strong hands-on experience with PyTorch, TensorFlow, or a similar deep-learning framework; experience with scikit-learn is also useful where appropriate.
Understanding of production ML concepts, including reproducibility, model versioning, deployment, monitoring, and troubleshooting.
Ability to design reliable, scalable, and maintainable software components and pipelines.

Understanding of model optimization, inference efficiency, and integration with production services or applications.

Experience developing and deploying ML solutions at scale, including end-to-end model lifecycle ownership.

Preferred Skills

PySpark, Apache Spark, and distributed processing for large-scale behavioral or event data.
MLflow or similar tooling for experiment tracking, model registry, reproducible training, and model lifecycle management.
Experience integrating Snowflake and Databricks within enterprise ML workflows.
Cloud experience with AWS, Microsoft Azure, or Google Cloud.
REST APIs, containers, CI/CD, automated testing, logging, and observability.
Experience with fraud detection, anomaly detection, behavioral modeling, identity or account-abuse signals, recommendation systems, or other large-scale ML applications.
Exposure to Generative AI, LLM integration, embeddings, or RAG as an additional advantage.

Education & Experience

Bachelor’s or Master’s degree in Computer Science, Software Engineering, Computer Engineering, or a related field, or equivalent practical experience.
At least 5 years of professional experience building, deploying, and supporting machine learning solutions at scale, or equivalent experience demonstrating the required engineering depth.
Demonstrated ability to take ownership of engineering deliverables and work through implementation, deployment, and operational issues.

Sales with Web Accessibility & Document remediation

at Ampera Technologies

Posted by Faisal AshrafNomani

Remote, Chennai

3 - 8 yrs

Best in industry

Sales

B2B Sales

Lead Generation

Business Development

Title: Sales with Web Accessibility & Document remediation

Experience: 3 to 5 Years

Location: Remote – Preferred Chennai

Employment Type: Full-Time

Notice period: Immediate Joiner

Role Overview

We are looking for a proactive and target-driven Sales Executive to drive business development and client acquisition for Web Accessibility and Document Remediation services across India. The ideal candidate will have experience in B2B sales, lead generation, and client relationship management, preferably in IT services, software solutions, document management, or digital accessibility. The role involves identifying prospective clients, understanding their digital accessibility requirements, presenting relevant solutions, and building a strong sales pipeline across industries.

Key Responsibilities

• Identify and develop new business opportunities for Web Accessibility and Document Remediation services across India.

• Generate leads through cold calling, email campaigns, LinkedIn outreach, networking, referrals, and other sales channels.

• Identify and engage decision-makers across IT, digital transformation, compliance, legal, content management, and accessibility teams.

• Promote Web Accessibility services, including website accessibility audits, WCAG compliance, accessibility testing, and website remediation.

• Promote Document Remediation services, including accessible PDF creation and remediation, document accessibility audits, and accessibility improvements for digital documents.

• Understand customer requirements and recommend suitable accessibility solutions based on business needs.

Conduct prospect meetings, service presentations, and discovery calls to communicate the value of Ampera's offerings.

• Develop and maintain a healthy sales pipeline, managing opportunities from lead generation through negotiation and closure.

• Prepare proposals, quotations, and commercial presentations in coordination with internal teams.

• Build and maintain relationships with enterprise customers, government organizations, public sector entities, educational institutions, banks, healthcare organizations, and other potential clients. • Maintain accurate sales records, lead status, follow-ups, and opportunity forecasts using CRM tools or sales trackers.

• Track market developments, customer needs, and competitor offerings to identify new business opportunities.

• Achieve agreed monthly and quarterly lead-generation, sales pipeline, and revenue targets.

Required Skills and Qualifications

• 3–5 years of experience in B2B sales, business development, or IT services sales.

• Proven experience in lead generation, prospecting, client acquisition, and sales pipeline management.

• Strong communication, presentation, negotiation, and relationship-building skills.

• Ability to understand customer requirements and explain technical services in clear business terms.

• Experience with cold calling, email outreach, LinkedIn prospecting, and CRM tools.

• Ability to independently manage sales activities and work toward defined revenue targets.

• Willingness to travel within India for client meetings, if required.

Good to Have

• Experience selling Web Accessibility, digital accessibility, or Document Remediation services.

• Knowledge of WCAG, accessibility testing, PDF remediation, and accessible document standards.

• Familiarity with digital accessibility requirements relevant to websites, applications, and electronic documents.

• Experience selling IT consulting, software testing, digital transformation, or document management services.

• Existing B2B client relationships across India, particularly with enterprise or institutional customers.

• Experience working with government, public sector, BFSI, healthcare, education, or large enterprise accounts. Key Performance Indicators (KPIs)

• Number of qualified leads generated.

• Number of prospect meetings and sales opportunities created.

• Sales pipeline value and conversion rate.

• New client acquisition and revenue generated. • Proposal-to-closure ratio.

• Achievement of monthly and quarterly sales targets.

Preferred Candidate Profile

• Proactive, self-motivated, and target-oriented.

• Strong ability to identify new customers and develop business relationships.

• Comfortable with outbound sales and consultative selling.

• Able to communicate the business value of accessibility and document remediation services.

• Capable of managing the complete sales cycle independently while working during working hours.