
Consultant/ Sr. Consultant - Generative AI
- Pune, Maharashtra
- Permanent
- Full-time
- Develop and optimize transformer-based NLP models, including LLM fine-tuning, evaluation, and prompt engineering.
- Build and deploy RAG pipelines by integrating LLMs with vector databases (FAISS, Pinecone, Chroma, Azure AI Search).
- Implement multi-agent orchestration frameworks (LangChain, LlamaIndex, AutoGen, CrewAI) for advanced workflows.
- Apply LLM evaluation and guardrail frameworks (Guardrails AI, TruLens, RAIL) to ensure reliability and safety.
- Design and maintain end-to-end ML pipelines: ingestion, preprocessing, training, deployment, and monitoring.
- Apply MLOps/LLMOps best practices for automation, observability, and reliability in Azure ML or equivalent platforms.
- Work with knowledge graphs (Neo4j, RDF/OWL) and integrate them into GenAI workflows.
- Build scalable pipelines and workflows using Databricks integrated with cloud platforms.
- Write clean, production-grade code in Python, including microservices, REST APIs, and FastAPI.
- Collaborate cross-functionally with data, cloud, and product teams to solve business problems with AI.
- 3–5 years of experience in AI/ML engineering.
- Proficiency in deep learning frameworks (PyTorch, TensorFlow).
- Solid experience with transformer models, LLMs, and RAG architectures.
- Familiarity with multi-agent orchestration and observability tools (LangSmith, Arize, W&B).
- Strong coding expertise in Python, APIs, and microservices.
- Hands-on experience with Databricks and enterprise ML workflows.
- Familiarity with knowledge graphs and their applications in AI.
- Experience with CI/CD pipelines and cloud-native deployments.
- Strong analytical skills and ability to link AI to business outcomes.
We are seeking a Senior AI Engineer / Architect to lead the design and implementation of enterprise-scale AI solutions. This role involves architecting Agentic AI systems, advanced RAG pipelines, AI-driven governance solutions, and multi-agent frameworks, while mentoring junior engineers and shaping the AI capability roadmap.What You'll Do :
- Architect and optimize transformer-based NLP and LLM systems, including advanced fine-tuning and evaluation.
- Lead the design of RAG architectures integrated with enterprise knowledge bases, vector databases, and knowledge graphs.
- Build and optimize AI agents and multi-agent systems that combine APIs, tools, and reasoning capabilities.
- Drive adoption of LLM guardrails, safety frameworks, and evaluation pipelines to ensure trustworthiness and compliance.
- Oversee the implementation of large-scale ML/GenAI pipelines with a focus on scalability, reliability, and robustness.
- Provide technical leadership in knowledge graphs, graph ML, and KG-GenAI integrations.
- Architect and optimize workflows on Databricks and integrate with cloud-native AI deployments (Azure, AWS, GCP).
- Drive adoption of observability/LLMOps practices (LangSmith, W&B, Arize) for production systems.
- Champion enterprise AI security, compliance, and governance standards (GDPR, HIPAA, SOC2).
- Mentor junior and mid-level engineers; guide code reviews, architecture decisions, and solution design.
- Partner with leadership to align AI solutions with strategic business outcomes.
- 5+ years of AI/ML engineering experience with proven enterprise delivery.
- Deep expertise in transformer models, LLMs, agentic AI, and multi-agent orchestration frameworks.
- Strong background in building and optimizing RAG systems at scale.
- Advanced proficiency with Databricks, Azure ML, and cloud-native AI platforms.
- Expert-level coding in Python, with advanced experience in microservices, REST APIs, and FastAPI.
- Strong experience in knowledge graphs, graph ML, and enterprise AI integration.
- Proven leadership in LLM evaluation, observability, and governance.
- Awareness of enterprise compliance and security frameworks relevant to AI.
- Strategic thinker with the ability to connect AI/ML capabilities to real business outcomes.