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
Frameworks & Libraries
PyTorch
TensorFlow
Scikit-learn
Hugging Face
LangChain
LangGraph
LlamaIndex
AutoGen
CrewAI
Semantic Kernel
Cloud & Infrastructure
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