Position: GenAI, Data engineer, ML engineering : Generative AI / Agentic AI Engineer / Data Engineer / ML Engineer Job Profile Specification: Generative AI / Agentic AI Engineer / Data Engineer / ML Engineer (56 years) Role summary:

- Senior-level engineer (56 years of professional experience) focused on designing, building, and deploying production-grade generative AI and agentic-AI solutions.
- Responsible for delivering secure, scalable, and business-oriented AI systems that operate on structured and unstructured data and enable AI-driven decision-making - Build and operate scalable, reliable data pipelines on Azure. Develop batch and streaming ingestion, transform data using Databricks (PySpark/SQL), ADF, enforce data quality, and publish curated datasets for analytics and ML.
- Design, development, and deployment of ML solutions at scale. Drive architecture, mentor the team, and integrate advanced AI (including LLMs) into enterprise workflows Required experience - 56 years of industry experience in software engineering and AI-related roles.
- Minimum 2-3 years of direct experience with Generative AI and Large Language Models (LLMs).

Key Responsibilities: GenAI

- Architect, develop, test, and deploy generative-AI solutions (online/offline LLMs, SLMs, TLMs) for domain-specific use cases.
- Design and implement agentic AI workflows and orchestration using frameworks such as LangGraph, Crew AI, or equivalent.
- Integrate enterprise knowledge bases and external data sources via vector databases and Retrieval-Augmented Generation (RAG).
- Build and productionize ingestion, preprocessing, indexing, and retrieval pipelines for structured and unstructured data (text, tables, documents, images).
- Implement fine-tuning, prompt engineering, evaluation metrics, A/B testing,



and iterative model improvement cycles.
- Conduct/model red-teaming and vulnerability assessments of LLMs and chat systems using tools like Garak (Generative AI Red-teaming & Assessment Kit).
- Collaborate with MLOps/platform teams to containerize, monitor, version, and scale models (CI/CD, model registry, observability).
- Ensure model safety, bias mitigation, access controls, and data privacy compliance in deployed solutions.
- Translate business requirements into technical designs with transparent performance, cost, and safety constraints.

Data Engineer

- Design, build, and maintain ETL/ELT pipelines in Azure Data Factory and Databricks across Bronze Silver Gold layers/Medallion Architecture.
- Implement Delta Lake best practices (ACID, schema evolution, MERGE/upsert, time travel, Z-ORDER).
- Write performant PySpark and SQL; tune jobs (partitioning, caching, join strategies).

Machine Learning engineer

- Machine Learning: Deep understanding of supervised, unsupervised, and reinforcement learning, model evaluation, and feature engineering.
- Deep Learning: Proficiency with TensorFlow, PyTorch, Keras; hands-on with CNNs, RNNs.
- Programming: Expert in Python (NumPy, Pandas, scikit-learn, etc.); R exposure acceptable.

Required Skills and Experience

- Strong proficiency in Python and experience with ML/AI libraries (scikit-learn, TensorFlow, PyTorch, Hugging Face ecosystem).
- Hands-on experience with LLMs, RAG,



vector databases, and retrieval pipelines.
- Practical experience deploying agentic workflows and building multi-step, tool-enabled agents.
- Experience using Garak (or similar LLM red-teaming/vulnerability scanners) to identify model weaknesses and harden deployments.
- Demonstrated experience implementing content filtering / moderation systems.
- Solid skills working with structured and unstructured data and advanced feature engineering.
- Familiarity with cloud GenAI platforms and services (Azure AI Services preferred; AWS/GCP acceptable).
- Experience building APIs/microservices; containerization (Docker), orchestration (Kubernetes).
- Strong understanding of model evaluation, performance profiling, inference cost optimization, and observability.
- Good knowledge of security, data governance, and privacy best practices for AI systems. Preferred / differentiating qualifications - Hands-on fine-tuning experience and parameter-effective tuning methods.
- Experience with multimodal models and retrieval-augmented multimodal pipelines.
- Prior work on agentic safety, tool-use constraints, LLM application firewalls, or human-in-the-loop systems.
- Familiarity with LangChain, LangGraph, Crew AI, or similar orchestration libraries. Values & behaviours - AI-first thinking: consistently seeks AI-enabled solutions to business problems.
- Data-driven mindset: makes decisions based on measurable insights and metrics.
- Collaboration & agility: effective contributor in cross-functional, fast-paced teams.
- Problem-solving orientation: looks beyond the obvious to unlock product and business value.
- Business impact focus: designs solutions with measurable outcomes and real adoption.
- Continuous .

📌 GenAI, Data engineer, ML engineering (Pune)
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