Sr Artificial Intelligence/Machine Learning Engineer

Job highlights

• depth of AI delivery is) . At least one production project involving LLM agents,skill building,or RAG pipelines - ideally on AWS Bedrock . AgentCore and / or Step Functions .
• Experience working directly with non-technical stakeholders - gathering requirements,presenting solutions .

Job Summary

Ciklum is looking for a Senior Artificial Intelligence/Machine Learning Engineer to join our team full-time in India.

We are a custom product engineering company that supports both multinational organizations and scaling startups to solve their most complex business challenges. With a global team of over 4,000 highly skilled developers, consultants, analysts and product owners, we engineer technology that redefines industries and shapes the way people live.

About the role:

As a Senior Artificial Intelligence/Machine Learning Engineer, become a part of a cross-functional development team engineering experiences of tomorrow.

Responsibilities

• Harness Engineering - Core

  • Harness engineering - Excellent understanding of designing agent harnesses: the control loop, tool and skill surface, context and state management, and human-in-the-loop checkpoints around an LLM
  • AWS Step Functions with ASL - Author Amazon States Language state machines to orchestrate agent steps, branching, retries, parallelism and human-approval gates (e.g. task-token waits); invoke AgentCore runtimes and Lambda from workflows
  • Deterministic vs LLM control - Design workflows where policy gates and rules decide routing and the LLM only proposes, with explicit exception, timeout and loop-guard paths
  • Amazon Bedrock AgentCore - Host, operate and scale agent workloads on AgentCore Runtime; understand lifecycle, scaling and invocation patterns, and expose tools via AgentCore Gateway
  • AgentCore Observability - Hands-on tracing, metrics and logging of agent runs (AgentCore, CloudWatch, Datadog) with a per-step audit trail to debug and monitor production workflows
  • AgentCore Evaluation - Hands-on design and running of evaluations (AgentCore Evaluations, golden-dataset regression tests) to measure agent quality before and after each release
  • Guardrails - Hands-on configuration of Amazon Bedrock Guardrails and policy controls for safety, prompt-injection defence, PII and hallucination mitigation, with human-in-the-loop review where required
  • Agentic AI with Claude
  • Claude Code / Cowork - Experience designing, deploying and scaling AI-powered production workflows using Claude Code and/or Cowork
  • LLM API fundamentals - Tool use, function calling, streaming and context management
  • Skill building - Design and publish reusable skills consumable by agent workflows
  • MCP (Model Context Protocol) - Build, host and consume MCP servers (e.g. FastMCP on AgentCore); expose and integrate tools for agent use, with agents reaching data only through MCP/API, never directly
  • Multi-agent coordination - Task delegation and result aggregation across agents (A2A)
  • RAG pipelines - Vector databases, chunking, embeddings and retrieval
  • Prompt engineering - System prompts, evaluation and iteration

• Front End

  • AG-UI - Build agent-facing user interfaces on the AG-UI protocol, connecting the front end to running agents for streaming responses, tool-call visibility, shared state and human approval steps

• Back End

  • Python 3.10+ - async/await, type hints
  • FastAPI or equivalent Python web framework
  • Basic SQL - query, filter, join; awareness of vector DBs (pgvector, Pinecone, OpenSearch)
  • NoSQL - Working knowledge of document/key-value stores (e.g. Amazon DynamoDB): data modelling, access patterns, and using them for agent run state and audit trails
  • REST API design, JWT / OAuth2
  • Workflow Automation
  • Decompose manual business processes into agent workflows
  • Document processing - PDF, Excel, unstructured data extraction and classification

• AI Engineer

  • OCR - Extract text, tables and layout from scanned and image-based documents (e.g. AWS Textract), and feed the output into agent workflows
  • Enterprise system integration - SharePoint, email, and external systems via APIs and MCP

• Infrastructure

  • AWS services - Lambda, ECS, RDS, S3, IAM - support agent deployment and data storage needs
  • Terraform / Infrastructure as Code - Provision and promote AgentCore, Step Functions, Lambda and supporting AWS resources across environments
  • Docker and containerisation
  • GitHub Actions CI/CD

  Requirements

• Proven experience in software engineering, with hands-on AI engineering shipped to production (years of experience are not the criterion; depth of AI delivery is)

• At least one production project involving LLM agents, skill building, or RAG pipelines - ideally on AWS Bedrock

• AgentCore and/or Step Functions

• Direct experience working with enterprise customers to design, deploy and scale AI-powered production workflows using Claude Code / Cowork

• Track record of owning and delivering solutions end-to-end with minimal guidance

• Experience working directly with non-technical stakeholders - gathering requirements, presenting solutions

• Consulting or client-facing background is an advantage

  Desirable

• Kubernetes

• TypeScript

• Fine-tuning and model evaluation

• CRE domain knowledge

Disclaimer: This job posting and Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.