AI Engineer

Neutrinoiq; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Neutrinoiq is seeking an experienced AI/ML Engineer to design, deploy, and scale machine learning systems for a next-generation financial advisory platform. You will own AI systems end-to-end from problem framing to deployment, monitoring, and optimization, with emphasis on reliability and business impact.

You will build LLM-powered workflows, work with vector databases, and integrate models into production via secure APIs while ensuring explainability and compliance.

Qualifications

• 3-5 years of hands-on experience building and deploying production ML systems.
• Strong coding skills in Python, Linux, version control, and databases.
• Experience designing end-to-end ML architectures and production pipelines.

Responsibilities

• Design and deploy ML pipelines for automated financial planning and advisor recommendations.
• Architect scalable ML and LLM systems for high-volume data.
• Turn ML models into secure, production-ready features via APIs.
• Monitor, retrain, and optimize models based on usage and behavior.
• Ensure outputs are explainable and compliant with data privacy standards.

Skills

Python

Linux

Distributed systems

LLM architectures

MLOps

Tools

Kubernetes

AWS boto3

MLflow

fastAPI

Kafka

Job description

About the Role

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.

You

• 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

What You'll Do

• 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

What We Expect From You

• 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, TensorFlow, 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 AutoGen, LangChain 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

Preferred (Not Required)

• 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

Must-Haves (Non-Negotiable)

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.

Why This Role?

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 that moves fast, solves problems at scale, and redefines how financial advice is delivered.