AIML Bedrock

Job Summary

AI/ML Engineer

This role involves working closely with cross-functional teams, understanding business needs, and delivering AI-driven solutions that enhance automation, insights, and customer experience.

Roles and Responsibilities

AI/ML & GenAI Development

• Design, build, and deploy

  GenAI applications and LLM-powered agents

  using

  AWS Bedrock

  .
• Develop prompt-engineered workflows, RAG pipelines, embeddings, and agentic automation.
• Implement scalable ML models using Python, ensuring efficiency, accuracy, and production readiness.

AWS Cloud & AI Platform Integration

• Work with AWS services such as

  Bedrock, Lambda, S3, Glue, API Gateway, DynamoDB

  for AI workflows.
• Integrate LLMs into existing applications using APIs, SDKs, and cloud-native tools.
• Optimize model performance, latency, cost, and reliability on AWS.

Solution Engineering & Delivery

• Understand client requirements and translate them into

  AI/ML solution designs

  .
• Build POCs, prototypes, and production-ready AI solutions for multiple client projects.
• Support post-deployment tasks including fine-tuning, versioning, and improvements.

Collaboration & Stakeholder Management

• Work with data engineers, product teams, and delivery managers to build cohesive AI-driven systems.
• Participate in requirement discussions, solution reviews, and architectural decisions.
• Communicate insights, model outputs, and recommendations clearly to technical and non-technical stakeholders.

Quality, Documentation & Governance

• Maintain clean code, detailed documentation, and model governance best practices.
• Ensure compliance with data privacy, security, and responsible AI guidelines.
• Monitor model performance and continuously improve AI pipelines.

Required Skills and Qualification

• 8+ years of overall technology experience

  , with

  4+ years in AI/ML development

  .
• Strong experience with

  AWS Bedrock, GenAI models, LLM Agents, and AI workflows

  .
• Hands-on expertise in Python (NumPy, Pandas, scikit-learn, LangChain, etc.).
• Experience with building AI microservices, APIs, or cloud-based applications.
• Good understanding of ML algorithms, NLP, vector stores, embeddings, and AI evaluation.
• Experience working in a

  service-based delivery model

  supporting multiple client projects.
• Strong communication and problem-solving skills.

Preferred Skills

• Experience with RAG frameworks, LangChain, LangGraph, or LlamaIndex.
• Exposure to AWS SageMaker, Step Functions, or MLOps pipelines.
• Knowledge of A/B testing, model observability, and prompt optimization.
• Prior client-facing experience.