Ai Ml Engineer

Job Summary

We are looking for a highly skilled AI/ML Engineer with a strong background in Machine Learning and hands-on experience in Generative AI, AWS Bedrock, Bedrock Agents, Multi-Agent Orchestration, and Databricks.

The ideal candidate will have experience designing, developing, and deploying scalable AI/ML solutions and working with modern Generative AI architectures. This role will focus primarily on AI/ML and GenAI capabilities, rather than recommendation engines, player recommendation systems, or propensity modelling.

Experience with AI Governance will be an added advantage.

Key Responsibilities

• Design, develop, and deploy scalable AI/ML solutions addressing complex business problems.
• Develop and productionize Generative AI applications using foundation models, LLMs, and related frameworks.
• Work hands-on with AWS Bedrock and Bedrock Agents to build enterprise-grade GenAI solutions.
• Design and implement multi-agent architectures and orchestration frameworks for complex AI workflows.
• Develop AI/ML pipelines and solutions using Databricks and associated data/ML capabilities.
• Apply machine learning techniques including model development, evaluation, optimization, and deployment.
• Integrate LLMs and AI agents with enterprise data, APIs, applications, and business workflows.
• Implement appropriate approaches for prompt engineering, retrieval-augmented generation (RAG), model evaluation, and GenAI application development.
• Collaborate with data engineers, software engineers, architects, and business stakeholders to translate requirements into AI/ML solutions.
• Establish appropriate practices for model monitoring, performance evaluation, scalability, reliability, and responsible AI.
• Contribute to the design and implementation of AI governance, security, compliance, and responsible AI practices.
• Stay current with emerging developments in ML, GenAI, LLMs, agentic AI, and AI/ML platforms.

Primary Skills

Strong AI/ML Background

• Strong fundamentals and hands-on experience in Machine Learning and Artificial Intelligence.
• Experience developing and deploying ML models in real-world/production environments.
• Strong understanding of ML algorithms, model evaluation, feature engineering, and ML lifecycle management.
• Strong programming skills in Python and experience with relevant ML/AI frameworks.

Generative AI

• Hands-on experience building Generative AI / LLM-based applications.
• Experience with LLM application development, prompt engineering, RAG, embeddings, vector databases, and model evaluation.
• Understanding of LLM architecture, limitations, performance optimization, and production deployment.

AWS Bedrock & Bedrock Agents

• Hands-on experience with Amazon Bedrock.
• Experience working with Bedrock Agents and integrating foundation models into enterprise applications.
• Understanding of model selection, inference, orchestration, guardrails, and enterprise GenAI architecture on AWS.

Multi-Agent Orchestration

• Experience designing and implementing multi-agent / agentic AI systems.
• Understanding of agent-to-agent communication, task decomposition, tool/function calling, workflow orchestration, and agent coordination.
• Experience with one or more agent orchestration frameworks is desirable.

Databricks

• Strong hands-on experience with Databricks for data engineering, ML, and/or AI workloads.
• Experience with ML pipelines, model development/deployment, experiment tracking, and production ML workflows on Databricks.
• Familiarity with MLflow and the broader Databricks ML/AI ecosystem is desirable.

Secondary Skills

AI Governance

• Understanding of AI Governance, Responsible AI, and AI risk management.
• Exposure to model governance, explainability, transparency, security, privacy, and compliance considerations.
• Awareness of governance requirements for enterprise GenAI and agentic AI applications.
• Experience implementing AI guardrails, monitoring, evaluation, and governance frameworks is a plus.

Good to Have

• Experience with AWS cloud services and cloud-native AI/ML architectures.
• Experience with LLM evaluation and observability.
• Experience with vector databases and RAG architectures.
• Knowledge of MLOps/LLMOps practices.
• Experience with AI security and GenAI guardrails.
• Familiarity with open-source LLMs and frameworks.
• Experience buildi…