SR AI Engineer Consultant II-India

The Role

We are seeking a highly skilled Senior AI Engineer (P4-II) to design, develop, and deploy enterprise-scale Artificial Intelligence, Generative AI, and Agentic AI solutions. The ideal candidate will possess strong expertise in machine learning, large language models (LLMs), AI application engineering, cloud-native architectures, and MLOps practices.

This role requires hands-on technical leadership, the ability to drive projects independently, mentor junior engineers, and collaborate with business stakeholders to translate business problems into scalable AI solutions.

The Main Responsibilities

Key Responsibilities

AI Solution Development

• Design, develop, and deploy AI/ML and Generative AI solutions for enterprise use cases.
• Build and optimize Retrieval Augmented Generation (RAG) applications.
• Develop Agentic AI workflows using frameworks such as LangGraph, CrewAI, Semantic Kernel, AutoGen, or equivalent.
• Create scalable AI APIs and microservices using Python and modern backend frameworks.
• Integrate AI capabilities into enterprise platforms and business applications.

Machine Learning & Data Science

• Develop and deploy predictive models using supervised and unsupervised learning techniques.
• Perform feature engineering, model training, evaluation, and optimization.
• Build NLP, classification, recommendation, forecasting, and anomaly detection solutions.
• Implement model monitoring and performance measurement frameworks.

Generative AI & LLM Engineering

• Work with Azure OpenAI, OpenAI, Gemini, Claude, Llama, and other foundation models.
• Design prompt engineering strategies and evaluation frameworks.
• Implement advanced RAG, GraphRAG, memory systems, and agent collaboration patterns.
• Fine-tune models when applicable and evaluate model quality, safety, and performance.
• Drive Responsible AI practices and governance requirements.

Platform Engineering & MLOps

• Design AI deployment pipelines using CI/CD best practices.
• Implement MLOps and LLMOps frameworks for model lifecycle management.
• Build observability, logging, tracing, and performance monitoring capabilities.
• Optimize model costs and infrastructure utilization.
• Work with Kubernetes, Docker, and cloud-native deployments.

Technical Leadership

• Mentor junior AI engineers and data scientists.
• Establish engineering standards, code quality practices, and reusable AI components.
• Collaborate with architects, product owners, and business stakeholders.

Mandatory Skills

Programming

• Python (Expert)
• SQL
• REST APIs
• Git/GitHub

AI & Machine Learning

• Machine Learning
• Deep Learning
• NLP
• LLMs
• Generative AI
• Prompt Engineering

Agentic AI

• LangGraph
• CrewAI
• Semantic Kernel
• AutoGen
• Multi-Agent Architectures

RAG Technologies

• Azure AI Search
• Vector Databases
• Knowledge Graphs

Cloud Platforms

• Azure (Preferred)
• AWS
• GCP

MLOps / LLMOps

• MLflow
• Azure ML
• Databricks
• CI/CD Pipelines
• Docker
• Kubernetes

Data Platforms

• Databricks
• Spark
• Delta Lake
• Data Warehousing Concepts

Preferred Skills

• Microsoft Copilot Studio
• Enterprise Agent Platforms
• Shared Memory Architectures
• AI Governance & Compliance
• GraphRAG Implementations
• Knowledge Graphs
• Event-Driven Architectures
• API Gateway Integration
• OAuth & Security Frameworks

What We Look For in a Candidate

Required Competencies

• Strong problem-solving and analytical skills.
• Ability to independently drive projects.
• Technical mentoring capability.
• Excellent stakeholder communication.
• Architecture and design thinking.
• Business outcome orientation.
• Continuous learning mindset.

Education

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

Success Criteria

• Deliver production-grade AI solutions with measurable business impact.
• Drive adoption of Generative AI and Agentic AI across business functions.
• Reduce operational effort through intelligent automation.