GenAI, Data engineer, ML engineering 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 (5–6 years) Role summary: Senior-level engineer (5–6 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 5–6 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 clear 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-productive 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 learning: stays current with academic research, open-source tooling, and best practices.

Location: IN-MH-Pune, India-Blue Ridge-Hinjewadi (eInfochips) Time Type: Full time Job Category: Engineering Services Experience Level Senior Level

📌 Senior Generative AI, Data and Machine Learning Engineer (Pune)
🏢 Arrow Electronics India
📍 Pune