AI / ML Engineer — Applied LLMs & Retrieval (Only 24h Left)

7 days ago

Mumbai, India VantedgeAI Full time

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.

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