GEN AI Engineer

Job Summary

Preferred Skills

• Experience with Machine Learning and Deep Learning concepts.
• Knowledge of MLOps tools such as MLflow, Kubeflow, or Azure ML.
• Experience with Docker, Kubernetes, and CI/CD pipelines.
• Understanding of AI Security, Responsible AI, and Model Governance.
• Exposure to multimodal AI solutions involving text, image, audio, or video models.
• Educational Qualification
• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
• Soft Skills
• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder management abilities.
• Ability to work independently and within Agile teams.
• Strong collaboration and teamwork skills.
• Continuous learning mindset with a passion for emerging AI technologies.
• Microsoft Azure AI Engineer Associate Certification.
• Experience building enterprise AI assistants and copilots.
• Exposure to Retail, BFSI, Healthcare, Manufacturing, or E-commerce domains.
• Experience with Agentic AI, Multi-Agent Systems, and Autonomous Workflows.
• Contributions to open-source AI projects or published AI research.

Educational Qualification

Soft Skills

Key Responsibilities

• Design and develop Generative AI solutions using Large Language Models (LLMs).
• Build AI-powered chatbots, virtual assistants, knowledge assistants, and document intelligence solutions.
• Design and implement RAG (Retrieval-Augmented Generation) architectures.
• Develop AI Agents and multi-agent workflows for enterprise use cases.
• Integrate AI solutions with enterprise systems, APIs, and cloud services.
• Fine-tune, evaluate, and optimize foundation models for business requirements.
• Implement prompt engineering techniques to improve response quality and accuracy.
• Work with vector databases and semantic search solutions.
• Monitor model performance, latency, security, and scalability.
• Collaborate with data scientists, architects, developers, and business stakeholders.
• Ensure compliance with Responsible AI, security, and governance standards.

Programming

Generative AI & LLMs

AI Frameworks

RAG & Vector Databases

Cloud Technologies

Data & Integration

Qualifications

• Programming
• Python
• SQL
• REST APIs
• Git
• Generative AI & LLMs
• OpenAI GPT Models
• Azure OpenAI
• Anthropic Claude
• Google Gemini
• Llama Models
• Mistral Models
• Prompt Engineering
• Fine-Tuning
• Embeddings
• LLM Evaluation
• Token Management
• AI Frameworks
• LangChain
• LangGraph
• LlamaIndex
• Semantic Kernel
• CrewAI
• AutoGen
• RAG & Vector Databases
• RAG-Based Applications
• Knowledge Search Platforms
• Enterprise Copilots
• Pinecone
• ChromaDB
• Weaviate
• FAISS
• Azure AI Search
• Cloud Technologies
• Microsoft Azure
• AWS
• Google Cloud Platform (GCP)
• Azure AI Services
• Azure Machine Learning
• Azure OpenAI Service
• AWS Bedrock
• Vertex AI
• Data & Integration
• Data Pipelines
• ETL Processes
• Structured and Unstructured Data Processing
• API Integrations
• Enterprise Data Platforms