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Senior Software Engineer - AI Engineering

Bengaluru

Role Summary

Embeds with client teams, understands manual business workflows, and builds AI-agent-powered automation to replace them. Helps build a new agent harness platform from the ground up — to be built on Claude (Claude Code /

Cowork), AWS Step Functions (ASL) and Amazon Bedrock AgentCore, with an AG-UI front end — and then delivers production workflows on it. Owns the solution end-to-end — discovery, build, deploy, evaluate, iterate — working

directly with enterprise customers with minimal direction.

TECHNICAL REQUIREMENTS

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

Backend

• 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

October 2026 JLL Confidential

• 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

ADDITIONAL SKILLS

• Kubernetes

• TypeScript

• Fine-tuning and model evaluation

• CRE domain knowledge

EXPERIENCE

• 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

Location:

Scheduled Weekly Hours:

If this job description resonates with you, we encourage you to apply even if you don’t meet all of the requirements.  We’re interested in getting to know you and what you bring to the table!

At JLL, we harness the power of artificial intelligence (AI) to efficiently accelerate meaningful connections between candidates and opportunities. Using AI capabilities, we analyze your application for relevant skills, experiences, and qualifications to generate valuable insights about how your unique profile aligns with the specific requirements of the role you're pursuing.

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