Data Engineer

Data Engineer

Project Role : Data Engineer
Project Role Description : Design, develop and maintain data solutions for data generation, collection, and processing. Create data pipelines, ensure data quality, and implement ETL (extract, transform and load) processes to migrate and deploy data across systems.
Must have skills : Databricks Unified Data Analytics Platform
Good to have skills : Large Language Models (LLMs)
Minimum 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education

Role Summary / Description

AI Powered Tech Talent
Engineer role in AI LLM Technology Architecture. Hands-on engineering role focused on designing, building, integrating, testing and operationalizing enterprise-grade LLM, GenAI and agentic AI components across active client engagements.
Own platform-specific engineering on Databricks, translating high-level architecture into working, production-quality components for LLM-driven applications, RAG pipelines, multi-agent workflows and AI platform integrations.
Bring practical industry experience in financial services, healthcare, manufacturing, retail, telecom or life sciences to identify domain data, process constraints, controls and adoption risks while designing GenAI solutions that are safe, scalable and relevant.
Operate as a hands-on technical lead or engineering lead, contributing code, design decisions, reusable patterns and engineering documentation.

Key Responsibilities
Design and build LLM application components including prompts, tools, agents, orchestration flows, memory/context handling, retrieval pipelines and evaluation harnesses.
Build data-grounded agentic applications on the lakehouse implement RAG with Delta tables, Vector Search and governed features use MLflow for tracing, evaluation and model lifecycle deploy agents or models with Model Serving and enforce governance through Unity Catalog.
Implement data ingestion, parsing, chunking, enrichment, embeddings, vector search and retrieval workflows for structured and unstructured enterprise content.
Engineer safety and control components including PII detection/redaction, prompt-injection defenses, content filters, guardrails, authentication, authorization, lineage and audit logging.
Collaborate with architects, data engineers, product owners and security stakeholders to convert solution designs into tested, observable and maintainable software components.
Maintain technical artifacts such as component designs, integration specifications, deployment runbooks, evaluation results and reusable engineering patterns.

Required Qualifications
Bachelor s degree in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology or a related engineering discipline.
Hands-on coding experience in Python and strong understanding of APIs, distributed systems, CI/CD, testing, observability and secure SDLC practices.
Experience delivering AI/ML or data products in at least one industry domain such as financial services, healthcare, manufacturing, retail, telecom or life sciences.

Required Skills/ Experience
Hands-on experience with Databricks Mosaic AI, Model Serving, Agent Framework, MLflow tracing/evaluation, Vector Search, Unity Catalog, Delta Lake, Lakehouse Monitoring, Feature Store, Databricks Workflows, Jobs, notebooks and Model Training.
Strong understanding of LLM application architecture patterns including RAG, function/tool calling, agent orchestration, model invocation, prompt engineering, embeddings, vector databases and evaluation metrics.
Ability to implement traditional ML and GenAI components across ingestion, feature/data preparation, model integration, deployment, monitoring and continuous improvement.
Practical knowledge of security, privacy, governance, performance, scalability, reliability and cost controls for production AI systems.
Experience with Git-based development, automated testing, CI/CD pipelines, infrastructure-as-code and agile delivery in client-facing environments.

Good to Have Skills
Databricks Machine Learning, Data Engineer or Generative AI certification experience with Spark/PySpark, Delta Live Tables, Unity Catalog governance, LangGraph/LangChain, model fine-tuning and lakehouse cost/performance optimization.
Exposure to open-source frameworks such as LangChain, LangGraph, LlamaIndex, Haystack, MLflow, FastAPI, Docker and Kubernetes.
Experience with Responsible AI, model risk management, synthetic data generation, human-in-the-loop review, A/B testing and GenAI cost optimization.