Thinkahead1h ago

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GenAI Engineer

Gurugram, HaryanaRemoteFull Timemid

OtherGenai Engineer

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Quick Summary

Overview

AHEAD builds platforms for digital business. By weaving together advances in cloud infrastructure, automation and analytics, and software delivery,

Technical Tools

OtherGenai Engineer

AHEAD builds platforms for digital business. By weaving together advances in cloud infrastructure, automation and analytics, and software delivery, we help enterprises deliver on the promise of digital transformation.

At AHEAD, we prioritize creating a culture of belonging, where all perspectives and voices are represented, valued, respected, and heard. We create spaces to empower everyone to speak up, make change, and drive the culture at AHEAD.

We are an equal opportunity employer, and do not discriminate based on an individual's race, national origin, color, gender, gender identity, gender expression, sexual orientation, religion, age, disability, marital status, or any other protected characteristic under applicable law, whether actual or perceived.

We embrace all candidates that will contribute to the diversification and enrichment of ideas and perspectives at AHEAD.

We are looking for a GenAI-focused software engineer to design, build, and maintain production-grade AI workflows on top of AHEAD’s existing platforms, SDKs, and guardrails. This role sits within our internal eTech Engineering organization, focused on using AI to improve the Product Development Lifecycle (PDLC), Software Development Lifecycle (SDLC), and internal business workflows.

You will partner closely with product, platform, and domain stakeholders (e.g., data, applications, operations) to translate real-world internal problems into reliable, scalable, and secure GenAI solutions that build on established tools and patterns rather than bespoke infrastructure.

GenAI Workflow & Service Development

Design, implement, and maintain Python-based services and workflows that integrate LLMs and GenAI capabilities with internal platforms and applications.

Build and iterate on agentic and multi-step workflows using approved orchestration frameworks and platform patterns (e.g., LangGraph, AgentCore, LangChain).

Consume existing retrieval/RAG and search abstractions to improve response quality, grounding, and reliability, tuning parameters (top-k, scoring, filters) rather than re-implementing core retrieval infrastructure.

Develop robust tooling and APIs for agents, including clear input/output schemas, error contracts, versioning, and observability hooks.

Implement structured output handling (e.g., JSON schemas, tool calls) to ensure predictable behavior and easier downstream integration.

Platform Integration & Governance

Operate within established platform, security, and governance guardrails (RBAC, data access boundaries, PII handling, logging, audit) instead of building custom, one-off mechanisms.

Leverage existing platform SDKs, templates, and patterns for configuration, deployment, and monitoring of GenAI workloads.

Work in a cloud-native AWS and Azure environments (e.g., cloud native applications, environment variables, secrets management, logging/metrics/tracing), collaborating with platform teams as needed rather than owning core infra design.

Collaboration & Delivery

Partner with product managers, internal business stakeholders, and UX to translate problem statements and evaluation criteria into concrete, production-ready workflows.

Collaborate with data and application teams to integrate GenAI capabilities into existing systems (e.g., internal tools, portals, automation flows) with minimal disruption.

Participate in design reviews, code reviews, and architecture discussions, ensuring solutions are maintainable, observable, and aligned with platform standards.

Contribute to internal enablement (playbooks, examples, patterns) to help eTech Engineering and other teams become effective consumers of GenAI capabilities.

Operations & Continuous Improvement

Own the operational health of GenAI workflows you build: monitoring, alerting, troubleshooting, and iterative improvement.

Incorporate evaluation and guardrail checks (e.g., automated tests, evaluation harnesses, red/blue team feedback) into workflows to improve quality and safety over time.

Act as a “high adopter of AI to build AI”, continuously using AI tools (e.g., Glean, Devin, Windsurf, Claude) to accelerate design, development, testing, and documentation.

3–7 years of software engineering experience with significant hands-on development in Python for production services and workflows.

Practical, hands-on exposure to LLM/GenAI integration (e.g., AWS Bedrock, Azure AI, OpenAI, Anthropic), including:

Prompt and system design for reliability and control.

Handling structured outputs (JSON schemas, tool calls, function calling).

Experience implementing agentic or multi-step workflows using orchestration frameworks such as LangGraph, AgentCore, or LangChain.

Proven ability to design clear tool APIs for agents with well-defined input/output schemas, error handling contracts, and versioning strategies.

Experience working in a cloud-native AWS/Azure environment, including:

Lambda or similar serverless patterns.

Environment configuration and secrets management.

Logging, metrics, and basic observability/debugging.

Familiarity with RAG/retrieval patterns as a consumer, including:

Using existing vector/search abstractions.

Tuning retrieval parameters (top-k, similarity thresholds, filters).

Understanding tradeoffs in chunking, metadata enrichment, and indexing strategies.

Strong software engineering fundamentals: version control (Git), testing, code review, CI/CD-friendly patterns, and clean code practices.

Effective collaboration and communication skills, with the ability to work closely with product, UX, and domain experts to converge on pragmatic, production-ready solutions.

Additional / Preferred

Awareness of security, governance, and responsible AI in an enterprise context, including:

RBAC and data access boundaries.

PII and sensitive data handling.

Working within established platform guardrails and governance processes.

Demonstrated experience building on top of an existing platform/SDK (coding standards, templates, reusable components) rather than building custom platforms from scratch.

Experience integrating GenAI into business workflows or engineering workflows (e.g., SDLC/PDLC automation, internal tools, support workflows, data/analytics workflows).

Familiarity with MLOps, data platforms, or observability tools used to track quality, performance, and usage of GenAI features.

Experience working with globally distributed teams, especially collaborating across India–US time zones.

Evidence of being an early and high adopter of AI tools for your own development workflow (e.g., using code assistants, AI debuggers, documentation generators, or experimentation tools as part of daily practice).