HCLTech is hiring Gen AI Developer for Noida, Hyderabad & Pune location.
Please find below Job Description:
Overall Experience: 4 to 10 yrs
Location: Noida, Hyderabad, Pune
Skills: Gen AI, Agentic AI, LLM, RAG, MLops, Cloud.
- Gen AI Framework: LangChain, LlamaIndex, Hugging Face Transformers, AutoGen, CrewAI, or similar.
Notice Period: Immediate to 60 days
Key Responsibilities
Cross-team Architecture & Technical Leadership
- Architect and own the end-to-end Solution Design, working alongside a System Architect or alone for an Industry AI Solution Team, ensuring architectural consistency, pattern reuse, and alignment with enterprise standards.
- Serve as the primary technical authority for GenAI and Agentic AI architecture decisions within assigned teams, providing direction to embedded GenAI Developers and guiding their implementation work.
- Define and enforce architecture standards, design patterns, reference architectures, and guardrails for GenAI and Agentic AI development across teams.
- Lead architecture reviews, proof-of-concept evaluations, and technical design discussions for AI features and components.
Generative AI & Agentic AI Solution Design
- Design scalable, production-grade GenAI application architectures including Retrieval-Augmented Generation (RAG) pipelines, Agentic Workflows, LLM integration layers, prompt management systems, and response evaluation frameworks.
- Architect multi-agent systems and Agentic AI workflows: agent orchestration patterns, tool use, memory management, long-horizon task execution, and human-in-the-loop designs.
- Select and evaluate appropriate LLMs, embedding models, and AI frameworks (commercial and open-source) based on performance, cost, latency, and compliance requirements.
- Define LLM fine-tuning and adaptation strategies (RLHF, PEFT, LoRA, prompt tuning) where required, overseeing implementation by the developer team.
- Design LLMOps and AI observability pipelines — including evaluation frameworks, tracing, monitoring, and feedback loops for deployed AI systems.
Enterprise System Architecture
- Apply deep system architecture expertise to ensure GenAI solutions integrate seamlessly into existing enterprise platforms including CRM, ERP, workflow automation tools, and analytics systems.
- Design robust, secure API layers, microservices, and event-driven integration patterns that connect AI components with enterprise backends.
- Ensure AI architectures meet enterprise non-functional requirements: scalability, availability, latency, security, and disaster recovery.
- Collaborate with infrastructure and cloud teams to architect AI workloads on cloud platforms (Azure preferred: Azure OpenAI, Azure AI Studio, Azure ML) with appropriate cost and performance optimisation.
Data Engineering & AI Data Architecture
- Design and govern data pipelines supporting AI model inference and (where applicable) fine-tuning: ingestion, transformation, quality validation, and versioning.
- Define data architecture for AI: vector databases, knowledge graphs, embedding stores, and structured/unstructured data integration patterns.
- Collaborate with data engineering teams to ensure data pipelines meet quality, freshness, and compliance requirements for AI use cases.
Responsible AI, Governance & Security
- Embed AI fairness, transparency, explainability, and accountability principles into solution designs from inception.
- Define and enforce AI security architecture: prompt injection protection, data leakage prevention, model access controls, and audit logging.
- Ensure compliance with applicable AI governance frameworks, data privacy regulations (e.g., GDPR), and enterprise AI policies.
- Conduct AI risk assessments for new solutions and advise teams on responsible deployment practices.
Stakeholder Collaboration & Communication
- Collaborate closely with executive stakeholders, product managers, business analysts, and engineering leads to align AI architecture with business strategy and product roadmaps.
- Present complex AI architecture concepts clearly to both technical and non-technical audiences; produce architecture documentation, decision records, and design artefacts.
- Actively contribute to the broader IAIS architecture community — sharing learnings, patterns, and innovations across teams and programs.
Mandatory Qualifications & Skills
Architecture & Engineering Foundation
- Bachelor's or Master's degree in Computer Science, Software Engineering, AI, Data Science, or a related field.
- Proven experience as a Solution Architect or Enterprise Architect (5+ years), with a track record of designing large-scale, production enterprise systems.
- Strong command of system design principles: distributed systems, microservices, event-driven architecture, API design, cloud-native patterns.
- Experience architecting across cloud platforms, with strong preference for Azure (Azure OpenAI, Azure AI Studio, Azure ML, Azure API Management).
Generative AI & Agentic AI Expertise
- Deep hands-on experience with LLMs and GenAI application development — including RAG architecture, LLM integration, prompt engineering, and chain-of-thought design.
- Proven experience architecting Agentic AI systems and multi-agent workflows using frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, or equivalent.
- Strong understanding of LLM evaluation, observability, and LLMOps practices — including tracing tools, evaluation frameworks, and production monitoring.
- Experience with vector databases and embedding stores (Pinecone, Weaviate, Azure AI Search, FAISS, Chroma, or similar).
- Familiarity with LLM fine-tuning approaches (RLHF, PEFT, LoRA) and when to apply them versus prompt-based adaptation.
Programming & Technical Skills
- Strong Python proficiency — able to prototype, review, and guide implementation of GenAI components.
- Working knowledge of software engineering best practices: Git, CI/CD, automated testing, API design, containerization (Docker/Kubernetes).
Responsible AI & Governance
- Sound knowledge of Safe AI principles, AI security patterns, AI governance frameworks, and data privacy compliance.
- Ability to conduct AI risk assessments and embed governance into architecture decisions.
Communication & Leadership
- Demonstrated ability to lead technical discussions and drive consensus across multi-disciplinary teams.
- Effective at communicating architecture decisions, trade-offs, and recommendations to both technical and executive audiences.
- Experience mentoring developers and guiding teams through complex AI implementation challenges.
Preferred Qualifications
- Experience leading AI architecture across multiple concurrent teams.
- Familiarity with MLOps tooling and platforms (MLflow, Azure ML Pipelines, Weights & Biases).
- Exposure to multimodal AI models (vision-language, speech-to-text, document AI).
- Experience with enterprise AI governance and compliance frameworks (EU AI Act, NIST AI RMF, or equivalent).
- Hands-on experience with AI-assisted development workflows and coding agents (e.g., GitHub Copilot, Cursor, or custom coding agents).
Prior experience in a product architecture role delivering Industry AI Solutions at enterprise scale.
