AI Engineer - AWS Bedrock Agents/Google Agentspace

About the Role:

We are building a next-generation AI Engineering function that delivers scalable GenAI solutions across our financial services ecosystem. As an AI Engineer, you will be responsible for conceptualizing, designing, building, and deploying end-to-end AI applications using AWS Bedrock, Bedrock Agents, Google A2A, Agentspace, and our enterprise AWS data platform. You will work closely with Data Science, Data Engineering, Platform Engineering, and Business teams to convert ideas into production-grade AI products.

Key Responsibilities:

A. AI Solution Design & Development:

- Build end-to-end AI applications using AWS Bedrock, Bedrock Agents, Google A2A, and Agentspace.

- Design and develop agentic workflows, reasoning chains, retrieval pipelines, and orchestration logic.

- Integrate LLMs with enterprise data sources (RDS, S3, DynamoDB, Redshift, Elasticsearch, etc.).

- Develop contextual retrieval systems (RAG), knowledge bases, and embeddings pipelines.

- Build modular, reusable components for prompts, agents, tools, evaluators, and guardrails.

B. Productionization & Deployment:

- Deploy scalable AI applications on AWS using Lambda, ECS, EKS, API Gateway, Step Functions, etc.

- Implement CI/CD pipelines for AI workloads.

- Build monitoring, logging, model evaluation, and model governance workflows.

- Ensure application-level SLAs, performance benchmarks, and latency requirements.

C. AI Infrastructure & MLOps:

- Work on vector databases (Pinecone, FAISS, Amazon OpenSearch Vector Store).

- Implement model evaluation pipelines, safety testing, prompt testing, and regression tests.

- Manage model lifecycle (versioning, fine-tuning, updates, rollback).

- Optimize cost, performance, and inference strategies (distillation, caching, batching).

D. Collaboration & Business Delivery:

- Understand business objectives and convert them into AI use cases.

- Work closely with Data Science to integrate ML/analytics outputs into GenAI flows.

- Partner with Engineering teams for enterprise integration, APIs, event-driven architectures.

- Ability to take a use case from idea - prototype - MVP - production.

Required Skills & Experience:

A. Technical Skills:

- Hands-on experience with AWS Bedrock / Bedrock Agents OR similar LLM orchestration stacks.

- Experience with Google A2A or Agentspace preferred (or willingness to learn fast).

- Strong programming skills in Python (FastAPI/Flask, LangChain, Bedrock SDK, etc.).

- Experience in building and deploying microservices on AWS.

- Solid understanding of vectors, RAG, prompt engineering, embeddings, semantic search.

- Knowledge of AWS services: Lambda, ECS/EKS, S3, Step Functions, DynamoDB, CloudWatch.

- Experience with CI/CD tools (CodePipeline, GitHub Actions, GitLab CI).

- Experience integrating third-party APIs and enterprise systems.

B. Soft Skills:

- Strong problem-solving skills and ownership mindset.

- Ability to translate business problems into technical requirements.

- Comfortable working in a fast-paced, experiment-driven environment.

- Excellent communication and stakeholder management.

Good to Have:

- Experience with containerization (Docker, Kubernetes).

- Exposure to ML models, classical MLOps, or feature pipelines.

- Familiarity with agentic AI frameworks (LangGraph, Haystack Agents, LlamaIndex Agents).

- Understanding of InfoSec, responsible AI, and guardrail frameworks.

Educational Background:

- Bachelors or Masters in Computer Science, Data Engineering, AI/ML, or related Mumbai

(ref:hirist.tech)