Senior AI/ML Engineer

• AI & ML Engineering
• Develop, deploy, and optimize machine learning and deep learning models.
• Build AI solutions for NLP, computer vision, predictive analytics, and intelligent automation.
• Integrate AI services into enterprise applications and internal platforms.

Generative AI

• Develop LLM-based applications, AI copilots, and RAG solutions.
• Implement prompt engineering, model evaluation, embeddings, and semantic search.
• Optimize model performance, scalability, and cost.

Agentic AI

• Design and deploy autonomous and multi-agent AI systems.
• Develop agent orchestration, planning, reasoning, memory, and tool integration.
• Implement human-in-the-loop approvals and enterprise workflow automation.

AI Operations (MLOps / LLMOps / AgentOps)

• Build CI/CD pipelines for AI models and agents.
• Monitor AI systems for performance, drift, hallucinations, security, and reliability.
• Manage model versioning, deployment, observability, and lifecycle operations.

AI Governance & Responsible AI

• Implement AI governance, model risk management, and Responsible AI practices.
• Maintain model documentation, validation, audit readiness, and compliance.
• Ensure fairness, traceability, maintainability, privacy, and regulatory adherence.

Agent Governance & Security

• Govern AI agent lifecycle, identity, access, and runtime controls.
• Implement guardrails, audit logging, and secure integration with enterprise systems.
• Protect AI solutions against prompt injection, data leakage, and adversarial attacks.

Collaboration

• Work with business, engineering, security, and architecture teams to deliver AI solutions.
• Mentor team members and promote AI engineering standards and best practice.

Technical Skills

AI & Machine Learning

• Machine Learning, Deep Learning, NLP, Computer Vision
• Predictive Analytics, Feature Engineering, Model Optimization

Generative AI

• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Prompt Engineering
• Embeddings and Semantic Search
• Fine-tuning Foundation Models

Agentic AI

• Autonomous and Multi-Agent Systems
• Agent Orchestration
• Planning and Reasoning
• Memory Management
• Tool Calling
• Human-in-the-Loop (HITL)
• Model Context Protocol (MCP)

Frameworks & Libraries

• PyTorch
• TensorFlow
• Scikit-learn
• Hugging Face
• LangChain
• LangGraph
• LlamaIndex
• AutoGen
• CrewAI
• Semantic Kernel

Cloud & Infrastructure

• Microsoft Azure AI Foundry
• Azure Machine Learning
• AWS Bedrock
• Amazon SageMaker
• Google Vertex AI
• Docker
• Kubernetes
• Terraform

Data & Integration

• SQL
• PostgreSQL
• MongoDB
• Apache Spark
• Vector Databases
• REST APIs
• FastAPI

AI Operations

• MLflow
• Kubeflow
• Azure ML Pipelines
• GitHub Actions
• Azure DevOps.