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About the Role

We're looking for a Senior AI/ML Engineer who thrives at the intersection of research and production — someone who doesn't just build models, but ships systems that scale. You'll own the full ML lifecycle: architecting robust data pipelines, training and rigorously evaluating models, and deploying them into high-throughput production environments. This is a role for engineers who want their work to move fast, break assumptions (not systems), and directly shape how AI gets built at scale. If you're energized by turning cutting-edge research into real-world impact, this is your next challenge.

Key Responsibilities

– Design, build, and maintain end-to-end ML pipelines — from raw data ingestion to model serving — with a focus on scalability and reliability.

– Develop, train, and rigorously evaluate ML/DL models using sound experimentation practices (A/B testing, offline/online metrics, statistical validation).

– Own the MLOps lifecycle: implement CI/CD pipelines for model training and deployment, version control for datasets and models, and automated retraining workflows.

– Deploy and monitor models in production using containerized, orchestrated infrastructure (Docker, Kubernetes), ensuring low-latency, high-availability inference.

– Collaborate cross-functionally with Data Engineering, Product, and Backend teams to translate business problems into scalable ML solutions.

– Optimize model performance for cost, latency, and accuracy trade-offs across cloud-based training and inference environments.

– Mentor and guide junior engineers and data scientists — conducting code reviews, sharing best practices, and raising the technical bar of the team.

– Stay current with emerging research (LLMs, generative AI, RAG architectures) and proactively identify opportunities to apply them to existing products.

Required Technical Skills

Languages

– Expert-level Python; working knowledge of R or C++ is a plus.

Frameworks

– TensorFlow, PyTorch, Scikit-Learn, Keras.

Data & Cloud

– Strong SQL and NoSQL fundamentals.

– Hands-on experience with at least one major cloud platform (AWS, GCP, or Azure), specifically their ML tooling — SageMaker, Vertex AI, or equivalent.

– Experience with distributed data processing frameworks: Spark, Hadoop.

MLOps & Deployment

– Proficiency with Docker and Kubernetes for containerized deployment.

– Experience building CI/CD pipelines for ML workflows.

– Familiarity with experiment tracking and pipeline orchestration tools like MLflow or Kubeflow.

Advanced / Nice-to-Have

– Practical experience with LLMs, prompt engineering, and Retrieval-Augmented Generation (RAG) architectures.

– Experience fine-tuning transformer-based models for domain-specific use cases.

Qualifications & Experience

– Bachelor's, Master's, or Ph.D. in Computer Science, Data Science, Artificial Intelligence, or a related quantitative field.

– 5+ years of hands-on experience building, training, and deploying production-grade AI/ML models at scale.

– Demonstrated track record of taking models from prototype to production in a real-world business setting.

What We Offer

– Competitive compensation, benchmarked to top-tier tech talent in the industry.

– Comprehensive health insurance coverage for you and your family.

– Annual learning & development stipend for courses, certifications, and conferences.

– A collaborative, high-ownership culture where engineering excellence is celebrated.

– The opportunity to work on cutting-edge AI systems with direct, visible impact.

About Tech Transient

Tech Transient is an AI consulting and digital transformation firm headquartered in Coimbatore, Tamil Nadu.

We partner with enterprises and growth-stage companies to design and deliver intelligent digital products — spanning mobile applications, cloud platforms, and AI-driven solutions. Our mission is to translate emerging technology into measurable business outcomes.

AuxoAI is seeking a Senior Applied Scientist to design and deploy structured knowledge systems that enable reliable, schema-grounded AI and agent reasoning.

This role sits at the intersection of large language models, knowledge graphs, semantic architectures, and hybrid retrieval systems. The ideal candidate will build systems that transform unstructured data into structured knowledge representations, enforce semantic constraints, and enable hybrid symbolic–neural reasoning in production environments.

You will play a key role in designing scalable semantic infrastructures that support advanced AI use cases such as GraphRAG pipelines, structured extraction, and agent reasoning workflows.

You will work on problems where existing architectures may not be sufficient and will experiment with new approaches that combine machine learning, knowledge graphs, semantic constraints, and classical AI techniques to build reliable, production-grade systems.

Location - Mumbai/Bangalore/Hyderabad/Gurgaon (Hybrid - 3 Days a week in Office)

Responsibilities:

• Design schema-guided information extraction systems using zero-shot and few-shot structured prompting, constrained decoding approaches such as JSON schema enforcement or grammar-based decoding, and function-calling or tool-driven extraction techniques.
• Develop recursive or multi-stage extraction pipelines capable of handling nested entities, hierarchical structures, and cross-document relationships.
• Build ontology-driven systems using frameworks such as LinkML, OWL, SHACL, or similar schema modeling tools, and implement knowledge representations using RDF triples or labeled property graphs.
• Design and optimize entity resolution algorithms using techniques such as blocking strategies, embedding similarity, and rule-based matching.
• Develop ontology alignment techniques and graph embedding models such as Node2Vec or TransE-style approaches where appropriate.
• Design hybrid retrieval architectures combining dense vector retrieval, sparse retrieval techniques, and graph traversal algorithms such as BFS, DFS, path ranking, and neighborhood expansion.
• Build validation systems that enforce schema conformance, detect semantic inconsistencies, and reduce hallucinated or invalid structured outputs.
• Integrate structured knowledge systems into GraphRAG pipelines, agent planning frameworks, and tool-selection workflows.
• Deliver production-grade semantic systems with clear targets for latency, scalability, reliability, and data integrity.

Requirements

• 5+ years of experience building production AI or machine learning systems.
• Strong experience designing and implementing knowledge graphs or ontology-driven architectures.
• Hands-on experience implementing structured extraction techniques, including grammar-constrained decoding, JSON schema enforcement, or AST-style parsing approaches.
• Experience building entity resolution systems beyond simple embedding similarity methods.
• Experience working with graph query languages such as SPARQL or Cypher and optimizing graph query performance.
• Familiarity with RDF, OWL, or property graph data models and semantic data architectures.
• Strong Python engineering skills, with emphasis on data validation, schema integrity, and system reliability.
• Experience designing hybrid symbolic and neural AI systems.

Nice to Have:

• Experience implementing graph algorithms such as PageRank, community detection, or shortest-path algorithms for reasoning chains.
• Experience building graph-enhanced retrieval systems such as GraphRAG.
• Experience designing compositional semantic extraction pipelines.
• Experience implementing reasoning engines or rule-based inference systems.
• Experience benchmarking and evaluating structural extraction accuracy and consistency.

ROLES AND RESPONSIBILITIES:

You will be responsible for architecting, implementing, and optimizing Dremio-based data Lakehouse environments integrated with cloud storage, BI, and data engineering ecosystems. The role requires a strong balance of architecture design, data modeling, query optimization, and governance enablement in large-scale analytical environments.

• Design and implement Dremio lakehouse architecture on cloud (AWS/Azure/Snowflake/Databricks ecosystem).
• Define data ingestion, curation, and semantic modeling strategies to support analytics and AI workloads.
• Optimize Dremio reflections, caching, and query performance for diverse data consumption patterns.
• Collaborate with data engineering teams to integrate data sources via APIs, JDBC, Delta/Parquet, and object storage layers (S3/ADLS).
• Establish best practices for data security, lineage, and access control aligned with enterprise governance policies.
• Support self-service analytics by enabling governed data products and semantic layers.
• Develop reusable design patterns, documentation, and standards for Dremio deployment, monitoring, and scaling.
• Work closely with BI and data science teams to ensure fast, reliable, and well-modeled access to enterprise data.

IDEAL CANDIDATE:

• Bachelor’s or Master’s in Computer Science, Information Systems, or related field.
• 5+ years in data architecture and engineering, with 3+ years in Dremio or modern lakehouse platforms.
• Strong expertise in SQL optimization, data modeling, and performance tuning within Dremio or similar query engines (Presto, Trino, Athena).
• Hands-on experience with cloud storage (S3, ADLS, GCS), Parquet/Delta/Iceberg formats, and distributed query planning.
• Knowledge of data integration tools and pipelines (Airflow, DBT, Kafka, Spark, etc.).
• Familiarity with enterprise data governance, metadata management, and role-based access control (RBAC).
• Excellent problem-solving, documentation, and stakeholder communication skills.

PREFERRED:

• Experience integrating Dremio with BI tools (Tableau, Power BI, Looker) and data catalogs (Collibra, Alation, Purview).
• Exposure to Snowflake, Databricks, or BigQuery environments.
• Experience in high-tech, manufacturing, or enterprise data modernization programs.