Job Title

AI / ML Engineer — Applied LLMs & Retrieval Python · PyTorch / TensorFlow · LangChain / Haystack · Vector DBs · AWS About Vantedge AI Vantedge AI is a YC-backed fintech re-imagining how private-credit, private-equity, and hedge-fund teams turn mountains of deal data into decisions. Our multi-agent workspace ingests everything from bond prospectuses and trustee reports to full data-rooms, then delivers structured models, crisp memos, and live dashboards in seconds—end-to-end secure and audit-ready . Founders Ravi – 20-year Wall Street credit-investing veteran; former CIO of Eight Capital (acquired by JC Flowers) and Wharton MBA. Vijay – ex-Goldman Sachs quant engineer; IIT Delhi & IIM Bangalore alumnus. Who you’ll work with A fast-moving team split across San Francisco, New York, and Mumbai—alumni of BITS Pilani, IITs, University of Mumbai, and high-growth fintechs . We ship weekly, measure everything, and give every developer true end-to-end ownership. Why join us Immediate impact: your code lands on the desks of leading credit funds within days. Deep domain + cutting-edge AI: solve hard problems at the intersection of finance and LLMs. Direct founder access: work side-by-side with Ravi and Vijay to shape both product and culture. Help us define how AI transforms Wall Street research—one agent at a time. What You’ll Do Integrate foundation models using AWS Bedrock, including Titan, Anthropic; OpenAI GPT-4o, and Google Gemini 1.5 Build retrieval pipelines with Pinecone, Elasticsearch, LangChain, and LlamaIndex Orchestrate multi-agent workflows using CrewAI, with memory, routing, and fallback logic Connect agents to internal systems via REST, GraphQL, and AWS Lambda Enforce guardrails with JSON/YAML schema validation, fallback prompts, and output filters Monitor performance and cost with LangChain, CloudWatch, and Grafana. You’ll Thrive Here If You Have 2–5 years building and deploying ML / NLP systems in Python. Hands-on experience with LLMs or Transformer-based models (OpenAI, Claude, Llama 2/3, etc.) and libraries such as LangChain, Haystack or LlamaIndex. Solid grasp of vector similarity search and embedding techniques; comfortable choosing indexes, distance metrics and chunking strategies. Familiarity with cloud-native ML ops on AWS (S3, Lambda / ECS, Step Functions, SageMaker or equivalent). Strong software-engineering habits: version control, automated testing, observability, CI pipelines. Clear written & verbal communication; you explain complex ML trade-offs to non-experts. Bonus points for experience with OCR / document AI, financial-domain modelling, RLHF / RLAIF, or security-focused model inferencing. Why Join Us Competitive salary plus meaningful early-employee equity . Hardware & learning stipend, flexible PTO. Zero bureaucracy—direct access to founders, rapid decision loops, work that ships and matters.