Role & responsibilities Collaborate with product and business teams to identify problems and define scalable, measurable AI/ML-driven solutions. Evaluate, fine-tune, and deploy production-ready models using the latest open-source and commercial model families (e.g., GPT-4o, Claude 3, Mixtral, LLaMA 3, Gemini). Develop and manage domain-specific generative models for tasks like summarization, classification, extraction, and generation using Transformers and LLMs. Build and maintain retrieval-augmented generation (RAG) pipelines, including vector databases (e.g., Weaviate, FAISS, LanceDB, Pinecone). Work with frameworks such as LangChain, LlamaIndex for prompt chaining, agent orchestration, and memory-enabled AI. Optimize for commercial KPIs such as inference cost, latency, model accuracy, and scalability. Benchmark and profile models to ensure high performance and reliability in production environments. Keep up to date with the evolving landscape of AI/ML and recommend tools, models, or practices that can improve team capabilities. Understand business requirements to propose and develop scalable and effective commercial solutions. Translate stakeholder needs into actionable data science roadmaps, prioritize deliverables, and plan resources across multiple initiatives. Assist, mentor, and train junior and mid-level data scientists to foster skill development, code quality, and cross-functional collaboration. Preferred candidate profile Programming: Expert-level proficiency in Python and common ML/AI libraries (NumPy, Pandas, scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers, LangChain). LLMs & NLP: Deep experience working with transformer-based architectures and LLMs, including prompt engineering, fine-tuning, and instruction-tuning. Generative AI: Practical knowledge in building GenAI applications using both open-source (Mistral, LLaMA) and API-based (OpenAI, Anthropic) models. RAG & Vector Search: Experience with RAG architecture and vector databases like FAISS, Pinecone, or Qdrant. Machine Learning & Deep Learning: Solid understanding of supervised, unsupervised, and reinforcement learning algorithms. Ability to work independently with minimal supervision in a dynamic and deadline-sensitive environment Strong leadership and mentorship skills, with experience guiding junior team members and fostering a collaborative, high-performing data science culture. What We Expect from You : Strong analytical thinking and curiosity to explore and challenge assumptions. Flexibility to work independently as well as collaboratively across a diverse, cross-functional team. Self-driven and organized with the ability to deliver in a fast-paced, evolving startup environment. Passion for innovation, continuous learning, and staying ahead of AI/ML trends. An entrepreneurial mindset with a bias toward experimentation and rapid iteration. Proven ability to lead initiatives, align stakeholders, and deliver impactful data science solutions end-to-end.
Role & responsibilities Lead research initiatives in graph-based ML and algorithms Develop novel approaches for graph-based problem solving Publish research findings in top-tier conferences and journals Transform research prototypes into production-ready solutions Build and optimize graph algorithms for scalable data processing Create efficient data models and implement graph database solutions Develop APIs and services around graph-based architectures Work on proof-of-concepts and prototype new features Collaborate with engineering teams on implementation Collaborate closely with product and Customer Success teams to understand Business Use Cases and broader product scope Contribute to architectural decisions and technology choices Preferred candidate profile B.Tech/M.Tech in Computer Science or related field from top-tier institutions 2+ years of Experience on software development using Java programming Language. 2-4 years of Experience with graph databases (Neo4j) Proficiency in Java. Good understanding of distributed systems Excellent problem-solving and analytical skills Preferred Skills: Experience with graph query languages (Cypher, Gremlin) Knowledge of graph visualization libraries Familiarity with cloud platforms (AWS) Experience with microservices architecture Technical Stack: Graph Databases: Neo4j Languages: Java Cloud: AWS/Azure Other tools: Git, Docker What Sets You Apart: Ability to work independently and take ownership Quick learner who can adapt to new technologies Strong bias for action and getting things done Passion for building scalable solutions Good communication skills in English