We are looking forLead Software Engineer (AIML Engineer NLP & Generative AI),
You'll make an impact by
- Architect and lead the development of NLP and Generative AI solutions, including LLM
- integration, RAG pipelines, and multi-agent frameworks.
- Design and optimize retrieval systems using knowledge graphs and vector databases,
- improving contextual accuracy and semantic relevance in RAG workflows.
- Apply advanced techniques (e.g., document chunking strategies, rerankers, hybrid retrieval, query rewriting, feedback loops) to enhance RAG chain precision and reduce hallucinations.
- Collaborate with ontology/domain experts to integrate structured knowledge bases and
- semantic relationships into the solution stack.
- Leverage modern frameworks like Lang Graph, Lang Chain, Llama Index, Smol Agents, and others for orchestrating agent-based and tool-augmented pipelines.
- Incorporate AWS Bedrock, Sagemaker, Azure ML Studio, Azure OpenAI Service, and Azure AI Foundry for cloud-native scalability and operational efficiency.
- Ensure high observability and maintainability of AI solutions through robust MLOps practices, logging, and model monitoring.
- Lead code/design reviews, mentor team members, and help shape long-term AI strategy and technical roadmaps.
- 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!
- Bachelor's or Master's degree in Computer Science, Machine Learning, AI, or a related field.
- 7+ years of AI/ML experience, with 3'4 years in NLP, and 2+ years in Generative AI
- applications.
- Expertise in designing production-grade RAG systems, including single-agent and multi-agent architectures.
- Solid understanding of LLM internals, prompt engineering, fine-tuning (LoRA, PEFT), and use of open-source and hosted foundation models.
- Experience with Knowledge Graphs, graph databases (e.g., Neo4j), and semantic enrichment strategies.
- Proficiency in Python and hands-on experience with frameworks like LangGraph, LlamaIndex, Transformers, and SmolAgents.
- Knowledge of vector databases (e.g., Azure AI Search, FAISS, Weaviate, Pinecone) and search optimization techniques.
- Familiarity with model observability tools, evaluation frameworks, and performance diagnostics.
- Strong experience with AWS and/or Azure managed services for AI development.
- Experience incorporating ontologies, taxonomies, and domain-specific schemas in knowledge enhanced AI systems.
- Prior exposure to industrial AI or Electrification/Power sector challenges is a strong plus.
- Knowledge of hybrid retrieval techniques combining symbolic and statistical methods.
- Strong stakeholder engagement and mentoring capabilities.
- Familiarity with compliance, safety, and ethical considerations in LLM deployments.
