Lead AIML Engineer – NLP & Generative AI

We are looking for: Lead Software Engineer (AIML Engineer – NLP & Generative AI),

You’ll make an impact by:

1. Architect and lead the development of NLP and Generative AI solutions, including LLM
2. integration, RAG pipelines, and multi-agent frameworks.
3. Design and optimize retrieval systems using knowledge graphs and vector databases,
4. improving contextual accuracy and semantic relevance in RAG workflows.
5. Apply advanced techniques (e.g., document chunking strategies, rerankers, hybrid retrieval, query rewriting, feedback loops) to enhance RAG chain precision and reduce hallucinations.
6. Collaborate with ontology/domain experts to integrate structured knowledge bases and
7. semantic relationships into the solution stack.
8. Leverage modern frameworks like Lang Graph, Lang Chain, Llama Index, Smol Agents, and others for orchestrating agent-based and tool-augmented pipelines.
9. Incorporate AWS Bedrock, Sagemaker, Azure ML Studio, Azure OpenAI Service, and Azure AI Foundry for cloud-native scalability and operational efficiency.
10. Ensure high observability and maintainability of AI solutions through robust MLOps practices, logging, and model monitoring.
11. Lead code/design reviews, mentor team members, and help shape long-term AI strategy and technical roadmaps.
12. Collaborate with product, cloud, software, and data engineering teams to deploy impactful AI capabilities in real-world settings.

Use your skills to move the world forward!

1. Bachelor’s or Master’s degree in Computer Science, Machine Learning, AI, or a related field.
2. 7+ years of AI/ML experience, with 3–4 years in NLP, and 2+ years in Generative AI
3. applications.
4. Expertise in designing production-grade RAG systems, including single-agent and multi-agent architectures.
5. Solid understanding of LLM internals, prompt engineering, fine-tuning (LoRA, PEFT), and use of open-source and hosted foundation models.
6. Experience with Knowledge Graphs, graph databases (e.g., Neo4j), and semantic enrichment strategies.
7. Proficiency in Python and hands-on experience with frameworks like LangGraph, LlamaIndex, Transformers, and SmolAgents.
8. Knowledge of vector databases (e.g., Azure AI Search, FAISS, Weaviate, Pinecone) and search optimization techniques.
9. Familiarity with model observability tools, evaluation frameworks, and performance diagnostics.
10. Strong experience with AWS and/or Azure managed services for AI development.
11. Experience incorporating ontologies, taxonomies, and domain-specific schemas in knowledge enhanced AI systems.
12. Prior exposure to industrial AI or Electrification/Power sector challenges is a strong plus.
13. Knowledge of hybrid retrieval techniques combining symbolic and statistical methods.
14. Strong stakeholder engagement and mentoring capabilities.
15. Familiarity with compliance, safety, and ethical considerations in LLM deployments.