GenAI, Data engineer, ML engineering

Position

GenAI, Data engineer, ML engineering

Job Description

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 pri