Senior GenAI Engineer - VectorDBand MySQL
HCLTech
Pune, Maharashtra, IndiaPosted 24 days agoDiscoveredMatch locked
Roles & Responsibilities
Architect and lead the development of
multi-agent AI systems
using frameworks such as LangGraph, CrewAI, and AutoGen — enabling autonomous reasoning, tool use, inter-agent coordination, and adaptive decision-making at enterprise scale.Design and operationalize
multimodal generative AI pipelines
that unify text, image, tabular, and graph data using transformer-based architectures (BERT, CLIP, LLaVA, T5, Whisper, GPT-4o, Gemini) for rich, cross-modal intelligence.Build production-grade
RAG and Graph-RAG systems
integrating vector databases (Pinecone, pgvector, OpenSearch) and knowledge graphs (Neo4j, AWS Neptune) for semantic retrieval, entity-aware reasoning, and grounded generation.Lead
LLM fine-tuning, prompt engineering, and model alignment
strategies — including RLHF, PEFT, LoRA, and instruction tuning — to adapt foundation models for specialized enterprise use cases.Establish robust
LLMOps and MLOps pipelines
on Databricks (AWS) using MLflow, feature stores, prompt evaluation frameworks, model lineage tracking, and continuous retraining workflows to ensure reliable AI delivery.Develop
high-performance Python backend services
for LLM inference orchestration, async job handling, streaming responses, and distributed data workflows supporting high-throughput Gen AI operations.Engineer
state, memory, and context management subsystems
that enable agents to reason temporally, maintain session continuity, manage long-context windows, and coordinate across tools and modalities.Implement
Responsible AI and AI governance
practices — including bias detection, hallucination mitigation, explainability dashboards, output safety guardrails, and compliance with data ethics standards — ensuring transparency and fairness of deployed models.Apply
traditional ML and statistical modeling
(regression, clustering, forecasting, ensemble methods) in hybrid architectures alongside LLMs for interpretable, explainability-first decision systems.Continuously
research, evaluate, and productionize
advancements in generative modeling, agentic AI, multimodal transformers, and frontier foundation models — benchmarking against enterprise-scale performance and safety requirements.
Roles & Responsibilities
Architect and lead the development of
multi-agent AI systems
using frameworks such as LangGraph, CrewAI, and AutoGen — enabling autonomous reasoning, tool use, inter-agent coordination, and adaptive decision-making at enterprise scale.Design and operationalize
multimodal generative AI pipelines
that unify text, image, tabular, and graph data using transformer-based architectures (BERT, CLIP, LLaVA, T5, Whisper, GPT-4o, Gemini) for rich, cross-modal intelligence.Build production-grade
RAG and Graph-RAG systems
integrating vector databases (Pinecone, pgvector, OpenSearch) and knowledge graphs (Neo4j, AWS Neptune) for semantic retrieval, entity-aware reasoning, and grounded generation.Lead
LLM fine-tuning, prompt engineering, and model alignment
strategies — including RLHF, PEFT, LoRA, and instruction tuning — to adapt foundation models for specialized enterprise use cases.Establish robust
LLMOps and MLOps pipelines
on Databricks (AWS) using MLflow, feature stores, prompt evaluation frameworks, model lineage tracking, and continuous retraining workflows to ensure reliable AI delivery.Develop
high-performance Python backend services
for LLM inference orchestration, async job handling, streaming responses, and distributed data workflows supporting high-throughput Gen AI operations.Engineer
state, memory, and context management subsystems
that enable agents to reason temporally, maintain session continuity, manage long-context windows, and coordinate across tools and modalities.Implement
Responsible AI and AI governance
practices — including bias detection, hallucination mitigation, explainability dashboards, output safety guardrails, and compliance with data ethics standards — ensuring transparency and fairness of deployed models.Apply
traditional ML and statistical modeling
(regression, clustering, forecasting, ensemble methods) in hybrid architectures alongside LLMs for interpretable, explainability-first decision systems.Continuously
research, evaluate, and productionize
advancements in generative modeling, agentic AI, multimodal transformers, and frontier foundation models — benchmarking against enterprise-scale performance and safety requirements.
Roles & Responsibilities
Architect and lead the development of
multi-agent AI systems
using frameworks such as LangGraph, CrewAI, and AutoGen — enabling autonomous reasoning, tool use, inter-agent coordination, and adaptive decision-making at enterprise scale.Design and operationalize
multimodal generative AI pipelines
that unify text, image, tabular, and graph data using transformer-based architectures (BERT, CLIP, LLaVA, T5, Whisper, GPT-4o, Gemini) for rich, cross-modal intelligence.Build production-grade
RAG and Graph-RAG systems
integrating vector databases (Pinecone, pgvector, OpenSearch) and knowledge graphs (Neo4j, AWS Neptune) for semantic retrieval, entity-aware reasoning, and grounded generation.Lead
LLM fine-tuning, prompt engineering, and model alignment
strategies — including RLHF, PEFT, LoRA, and instruction tuning — to adapt foundation models for specialized enterprise use cases.Establish robust
LLMOps and MLOps pipelines
on Databricks (AWS) using MLflow, feature stores, prompt evaluation frameworks, model lineage tracking, and continuous retraining workflows to ensure reliable AI delivery.Develop
high-performance Python backend services
for LLM inference orchestration, async job handling, streaming responses, and distributed data workflows supporting high-throughput Gen AI operations.Engineer
state, memory, and context management subsystems
that enable agents to reason temporally, maintain session continuity, manage long-context windows, and coordinate across tools and modalities.Implement
Responsible AI and AI governance
practices — including bias detection, hallucination mitigation, explainability dashboards, output safety guardrails, and compliance with data ethics standards — ensuring transparency and fairness of deployed models.Apply
traditional ML and statistical modeling
(regression, clustering, forecasting, ensemble methods) in hybrid architectures alongside LLMs for interpretable, explainability-first decision systems.Continuously
research, evaluate, and productionize
advancements in generative modeling, agentic AI, multimodal transformers, and frontier foundation models — benchmarking against enterprise-scale performance and safety requirements.
Other Requirements
null
Not included in the source posting: about the role, benefits.
Skills
artificial-intelligencellmawsraggenerative-aidatabricksmachine-learningmlopspythonmysql
Who can apply
The employer didn't state any visa, work authorization, citizenship or clearance requirements in this posting. Confirm with the employer before applying.
Read automatically from the employer's posting text. Always confirm with the employer — requirements can change after a job is published.