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ClosedPosted 18 days ago

Senior AI Agent Engineer

Scrabble & Jigsaw

Where
Bangalore Urban, India

This posting closed on 2026-10-23.

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Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
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  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 18 days ago

The posting

Senior AI Agent Engineer

## Company Overview Not specified.

## Job Summary We are seeking an experienced engineer to design and build secure, production-grade AI agents that seamlessly integrate with business systems. The role involves developing agents capable of retrieving trusted information, utilizing tools, and supporting human-approved workflows. The successful candidate will contribute to advancing AI capabilities within the organization, ensuring robust, safe, and efficient AI operations in production environments.

## Responsibilities - Build and operate the AI-agent runtime, ensuring reliable and scalable performance. - Design coordinator and specialist-agent workflows to optimize task execution and collaboration. - Create typed and versioned tool registries for consistent and maintainable integrations. - Integrate agents with backend services and data sources to enable comprehensive data access. - Implement conversation state management and purpose-bound memory to support contextual interactions. - Develop Retrieval-Augmented Generation (RAG) ingestion, retrieval, provenance, and citation systems. - Implement deterministic authorization and policy controls outside the AI models to enforce security and compliance. - Incorporate human confirmation steps for actions with significant impact. - Build evaluation suites and automated release gates to ensure quality and safety. - Establish observability, latency, and cost controls for operational efficiency. - Implement feature flags, kill switches, and graceful degradation mechanisms. - Support security reviews, production deployment, and incident response procedures. - Own canary releases, rollback procedures, and maintain operational runbooks for smooth deployment and maintenance.

## Qualifications - Strong Python engineering experience with expertise in async Python, FastAPI, Pydantic, SQLAlchemy, and Alembic. - Proven production experience with LangGraph, OpenAI Agents SDK, or comparable agent frameworks. - Deep understanding of Retrieval-Augmented Generation (RAG), embeddings, vector databases (e.g., pgvector), and source-grounded generation. - Hands-on experience with PostgreSQL, Redis, and queue-based asynchronous background processing. - Knowledge of REST APIs, Server-Sent Events, and distributed system fundamentals such as idempotency, retries, and timeouts. - Experience with model-provider integration platforms like OpenAI, AWS Bedrock. - Skills in model routing, fallbacks, context management, and prompt versioning. - Ability to manage token, latency, and cost controls effectively. - Proficiency in OAuth2, JWT, JWKS, and service-to-service authentication. - Expertise in PII minimization, encryption, secrets management, and least-privilege access. - Experience with Docker and cloud deployments, preferably on AWS. - Familiarity with OpenTelemetry, structured logging, metrics, tracing, and alerting. - Strong background in automated testing using pytest and managing integration-test environments.

## Preferred Skills - Experience in AI safety and evaluation, including regression testing, deterministic evaluation, and prompt-injection testing. - Familiarity with tools such as DeepEval, RAGAS, Promptfoo or equivalent. - Knowledge of adversarial and multilingual testing. - Experience in regulated or high-trust domains is advantageous. - Ability to design evaluation datasets and deployment gates. - Understanding that model tool selection is distinct from authorization.

## Experience - 7+ years of software engineering experience. - 2+ years building generative AI or large language model (LLM) systems. - Proven track record of deploying tool-using or RAG-based AI applications to production. - Strong background in backend and distributed systems fundamentals. - Experience in moving AI products from prototype to monitored production environments.

## Environment - The role involves working in a collaborative, fast-paced environment. - Likely involves remote, in-office, or hybrid work settings. - Requires adherence to security protocols and incident response procedures. - Focus on high-trust, regulated domains may involve additional compliance and security considerations.

## Salary Not specified.

## GrowthOpportunities Not specified.

## Benefits Not specified.

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