Mactores - Senior Product Engineer - Java/Python

Mactores; Bengaluru; On-site; INR 400,000 - 700,000; Full time

Job summary

Aedeon is seeking a Senior Product Engineer for the Agent Systems team in Bengaluru. You will design and ship agents that modernize enterprise systems, focusing on Python-based tooling, asynchronous services, and scalable workflows.

You’ll integrate with LLM APIs, manage containerized services on AWS, and lead quality through test-driven development. You will collaborate in a fast-paced product environment, participate in reviews, and ensure delivery deadlines.

Qualifications

• 5+ years of production Python experience with async patterns.
• Strong understanding of distributed systems concepts (state machines, retries, idempotency).
• Experience integrating with LLM APIs (streaming, function calling, structured output).
• Comfort with FastAPI and modern async web frameworks; solid testing practice with pytest.

Skills

Python

Async IO

Distributed systems

LLM integration

FastAPI

CI/CD

AWS

Docker

Kubernetes

Testing (pytest)

LangChain

GitHub Actions

Code quality

English proficiency

Tools

Docker

Kubernetes

FastAPI

AWS (EKS/ECS/Fargate)

S3

DynamoDB

Lambda

GitHub Actions

pytest

LangChain

OpenAI/Anthropic integration

Job description

About Aedeon
Aedeon is the agent-native modernization platform for the enterprise.

About Aedeon
Aedeon is the agent-native modernization platform for the enterprise. We turn the systems already running the business, applications, databases, data platforms, business rules, and workflows, into governed AI agents, grounded in a persistent Code Intelligence Graph of the customer's own code and verified through behavior-equivalence proof. Aedeon is delivered as a product, not a services engagement, and runs without mandatory forward-deployed engineers. As Senior Product Engineer on Aedeon\'s Agent Systems team, you\'ll build the agents that do the actual modernization work.

Aedeon\'s Product Surface Is The Agent Fleet Itself

• parsers that read legacy estates
• extractors that lift business rules out of code
• synthesizers that stand up agentic workflows
• verifiers that produce behavior-equivalence proof
• Agent Systems Development.
• Build and ship specialized agents in the Aedeon fleet: parsers, business-rule extractors, dependency mappers, test synthesizers, behavior replayers, and the orchestration that wires them together.
• Design agents that operate against the Code Intelligence Graph rather than generating from priors, so every agent action traces to a line of code.
• Implement governed autonomy: shadow, supervised, and autonomous stages with human-in-the-loop controls at every stage.
• Orchestrate frontier foundation models (Anthropic, OpenAI, Google) through the Aedeon Decision Model: pick the right model for each task, against the right slice of the graph, under explicit governance constraints.
• Release Ownership.
• Own the full delivery of assigned agents from prototype through deployment and post-release validation.
• Commit to sprint and release deadlines.
• Raise blockers and risks with enough lead time to mitigate them.
• Coordinate with platform and DevOps engineers to keep deployment pipelines clean and repeatable.
• Verification and Quality.
• Practice test-driven development.
• Write the tests for the agent\'s contract, governance constraints, and equivalence checks before the agent code that satisfies them.
• Build behavior-equivalence verification into every agent: dual-run tests, output diffing, equivalence certificates against production traffic.
• Write detailed test cases before deployment, covering functional flows, edge cases, regression scenarios, and integration touchpoints.
• Use AI tools to generate qualitative test coverage at scale and validate it for completeness.
• Maintain automated test suites (unit, integration, E2E) and integrate them into the CI/CD pipeline.
• Collaboration and Documentation.
• Write clear, maintainable Python with adequate documentation.
• Review pull requests thoroughly and provide constructive feedback.
• Document agent contracts, prompt structures, decision logic, and verification approaches in Confluence or equivalent.
• Share patterns and testing practices across the team.
• Promote a culture of release discipline and quality.

What are we looking for? :

• Core Python and Systems.
• Strong Python (4+ years production), async (asyncio), performance optimization, idiomatic code.
• Solid grasp of distributed systems concepts: state machines, retries, idempotency, eventual consistency.
• Experience integrating with LLM APIs (Anthropic, OpenAI, or similar) from production code: streaming, function calling, structured output, retries, prompt management.
• Comfort with FastAPI or equivalent async web frameworks.
• Cloud and Infrastructure.
• Working knowledge of AWS services: EKS, ECS Fargate, S3, DynamoDB, Lambda, Secrets Manager, CloudWatch.
• Experience with Docker and Kubernetes: writing Dockerfiles, Helm charts, and Kubernetes manifests.
• Comfort with CI/CD pipelines, GitHub Actions preferred.
• Comfort navigating multi-account AWS environments (dev, uat, prod).
• Testing and Automation.
• Test-driven development as a discipline.
• Tests written before the code, not after.
• Hands-on experience with pytest, integration testing, and E2E testing.
• Ability to design behavior-verification harnesses: dual-run, output comparison, equivalence proof.
• Experience using AI tools (Claude, Copilot, LLM-based test generators) to accelerate and improve test case quality.
• Experience integrating automated tests into CI/CD pipelines.
• Engineering Mindset.
• Test-first, release-disciplined, ownership-driven.
• Strong written and spoken English.
• You'll be in product reviews and customer-impacting design discussions.
• Available to work with US business-hour overlap from India.

You\'ll Be Preferred If

• Agentic AI frameworks: AWS Bedrock AgentCore, Strands SDK, LangChain, or similar.
• Temporal.io or other workflow orchestration engines.
• Graph databases (Neo4j) and Cypher query language.
• Amazon OpenSearch Service or Elasticsearch.
• Java exposure for working with the Java Analyzer component.
• Prior B2B SaaS product work with a structured release process.
• Domain exposure to enterprise modernization: mainframe, SAP, Oracle, or large Java / .NET estates.

(ref:hirist.tech)