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

  AI Monitoring & Observability

Desired Candidate Profile

• Work Experience

  Minimum:

  • Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related field.

  • 5+ years of software or AI/ML engineering experience.

  • 3+ years building and operating production AI solutions.

  • Strong Python programming and software engineering skills.

  • Experience with enterprise AI governance, cloud platforms, and secure AI development.

Skills

Python

Machine Learning

Deep Learning