AI Engineer

Were building a next-generation AI-powered platform for financial advisors. This isn't theory. Were solving real-world problems with real-world data and we need an experienced AI/ML Engineer who knows how to build, deploy, and scale machine learning systems that actually ship. You will own AI systems end-to-end from problem framing and architecture design to deployment, monitoring, and optimization. This role requires someone who can independently drive initiatives, make sound architectural decisions, and improve the reliability and scalability of our ML infrastructure.

From financial plan generation to LLM-powered automation and personalized insights, this role requires someone who understands both the tech and the business need behind it.

• Move fast, think clearly, and adapt without friction
• Love testing ideas, building prototypes, and iterating based on real user data
• Prefer action over meetings and always look for a better way to do things
• Want to be part of a product that redefines an entire industry
• Have technical understanding of how things work
• Clear and concise communication, collaborator, and coder
• Be a doer rather than a talker, going plus ultra

• Design and deploy ML pipelines for automated financial plan creation, personalized advisor recommendation, and create actionable insights tailored to client behavior
• Architect scalable ML and LLM systems capable of handling high-volume financial data
• Build LLM-powered workflows using:
  Python, Linux, Kubernetes, AWS boto3, Strands agent, Agent squad, dspy, mlflow, fastAPI, websockets, caching, bert, hugging face, notebooks, kafka and system design is a plus
• Work with vector databases (Pinecone, Weaviate, AWS specific, etc.) to support RAG pipelines
• Turn ML models into secure, production-ready features via APIs
• Tune, monitor, and retrain models based on usage and behavior
• Make sure outputs are explainable and in line with compliance and data privacy standards
• Collaborate with engineering and product teams to move from prototype to live feature quickly
• Conduct technical reviews of ML architecture and code
• Drive experimentation roadmap and hypothesis testing strategy

• 3-5 years of hands-on experience building and deploying production ML systems
• Strong coding skills in Python, Linux, VCS, Database
• Strong understanding of distributed systems and production infrastructure
• Experience designing end-to-end ML architectures (data ingestion training serving monitoring)
• Experience optimizing model performance and cost efficiency in cloud environments
• Deep understanding of LLM tradeoffs (fine-tuning vs RAG vs hybrid approaches)
• Experience implementing evaluation frameworks for LLM reliability and safety
• Familiarity with model governance and auditability in regulated industries
• Experience scaling ML systems under real user load
• Demonstrated ability to independently lead ML initiatives from concept to launch
• Experience with ML libraries like PyTorch, Tensor Flow, Scikit-learn, XGBoost, Sentence Transformers, Transformers, Streamlit etc.
• Direct, hands-on experience with LLMs not just playing with prompts, but integrating them into production workflows
• Familiar with Auto Gen, Lang Chain or similar orchestration tools
• Comfortable using embeddings and vector databases for search or memory
• Clear communicator who can explain what the model does and why it matters to both technical and non-technical teams
• Experience working in fast-paced, early-stage SaaS or startup teams

• Experience building AI systems in regulated industries (fintech, banking, wealth management)
• Experience working with structured and unstructured financial datasets (transactions, portfolio data, CRM data, planning data)
• Familiarity with financial planning concepts (AUM models, retirement projections, tax optimization, risk profiling)
• Familiarity with SOC 2, GDPR, SEC/FINRA considerations, or financial auditability standards
• Experience building systems with data lineage and model audit tracking

1. You must be currently based in India.

2. You must be available to work during U.S. Mountain Standard Time hours (India night shift).

3. You should be looking for a long-term, full-time opportunity with ownership, not a freelance or short-term contract.

You wont just be a cog in the system. You'll own a core part of the product, help shape the tech direction, and work directly with founders who value execution over theory. You'll help build something…