Senior Software Engineer

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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:

On-site –Bengaluru, KA

Scheduled Weekly Hours:

40

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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