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Lead GenAI Engineer
Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets. An ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner.
Please visit Fractal | Intelligence for Imagination for more information about Fractal
Location: Bay Area, California (Sunnyvale preferred)
Note: This position is not eligible for Immigration Sponsorship at this time.
Role Overview:
You will be the hands-on technical lead for a GenAI product at the intersection of large language models and enterprise analytics. This is a builder's role. You will personally architect and ship production systems, own delivery quality, and set technical direction for a distributed engineering team. This is not an advisory or architecture-only position.
Key Responsibilities
Hands-on GenAI Engineering & Delivery
Design, build, and ship production GenAI systems end to end. Remain hands-on in the codebase rather than directing from a distance.
Own the architecture across LLM orchestration, retrieval, agentic workflows, evaluation, and the analytics delivery layer.
Rapidly prototype against evolving requirements and harden successful approaches into production-ready systems.
Technical Leadership & Quality
Set technical direction for a distributed offshore engineering team and own the quality of what ships.
Establish evaluation, observability, and guardrails so system quality is measurable and defensible.
Required Experience
Production GenAI & Agentic Systems
8+ years of overall engineering experience, including 4+ years of hands-on experience building, shipping, scaling, and maintaining production GenAI or LLM systems. Experience limited to prototypes, proofs of concept, or research is not sufficient.
Demonstrated record of personally building RAG pipelines, multi-agent or agentic systems, and LLM-powered analytics or decision-support workflows, with concrete and quantified outcomes.
Deep hands-on expertise with agent orchestration frameworks such as LangGraph, LangChain, LlamaIndex, CrewAI, or the OpenAI or Anthropic agent SDKs.
Practical experience with modern GenAI patterns, including tool or function calling, structured outputs, hybrid and agentic retrieval, context engineering, prompt and evaluation optimization, and multi-agent coordination.
Hands-on experience using agentic coding harnesses and AI-assisted development tools such as Claude Code, Cursor, or equivalent as part of the build workflow.
Backend Engineering, Evaluation & Analytics
Strong Python skills and solid backend engineering fundamentals, including REST and streaming API design, asynchronous patterns, and containerized deployment with Docker and Kubernetes.
Production experience with LLM evaluation and observability, including offline and online evaluations, tracing, hallucination measurement, and quality measurement using RAGAS, LangSmith, or equivalent tools.
Sufficient fluency in data science, machine learning, and business intelligence to reason about analytical accuracy, data quality, and output reliability.
Leadership, Education & Location
Proven ability to operate in fast-moving, ambiguous environments where scope evolves.
Strong first-principles thinking, with the ability to evaluate trade-offs, challenge assumptions, and design modular and defensible systems.
US-based; PST or MST timezone required. Sunnyvale or the Bay Area is strongly preferred.
Bachelor's degree required. Master's degree in computer science, artificial intelligence, machine learning, or a related technical field preferred.
Preferred Experience
Hands-on experience with Model Context Protocol (MCP) or agent-native enterprise platforms.
Experience building and orchestrating custom agents, tools, and harnesses beyond off-the-shelf usage.
Background in analytics, business intelligence, or data products where AI intersects with enterprise reporting and decision-making.
Experience with Text-to-SQL or natural-language-to-analytics systems.
Consulting or client-embedded delivery experience in matrixed organizations with distributed teams.
Certifications across LLM or cloud AI platforms such as AWS Bedrock, Azure OpenAI, Anthropic Claude, or Google Vertex AI.
Experience with LLMOps tooling such as LangSmith, MLflow, Weights & Biases, or equivalent.
Pay:
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions, including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Fractal, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is: Up to $185,000. In addition, you may be eligible for a discretionary bonus for the current performance period.
Benefits:
As a full-time employee of the company or as an hourly employee working more than 30 hours per week, you will be eligible to participate in the health, dental, vision, life insurance, and disability plans in accordance with the plan documents, which may be amended from time to time. You will be eligible for benefits on the first day of employment with the Company. In addition, you are eligible to participate in the Company 401(k) Plan after 30 days of employment, in accordance with the applicable plan terms. The Company provides for 11 paid holidays and 12 weeks of Parental Leave. We also follow a “free time” PTO policy, allowing you the flexibility to take the time needed for either sick time or vacation.
Fractal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
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Skills Required
- Strong experience with LLMs (e.g., GPT-4, LLaMA, Claude, PaLM) and frameworks like Hugging Face, LangChain, LlamaIndex, or Haystack
- Experience fine-tuning foundation models for domain-specific tasks
- Experience implementing Retrieval-Augmented Generation (RAG) frameworks and enterprise retrieval systems
- Experience implementing agent-based/agentic architectures for autonomous task execution
- Proficiency in Python and ML libraries (PyTorch, TensorFlow, scikit-learn)
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)
- Familiarity with MLOps practices and tools for model deployment and monitoring
- Strong grounding in NLP techniques (embeddings, vector databases, text classification, summarization, QA)
- Proven track record delivering scalable GenAI solutions in enterprise environments
- Strong problem-solving and communication skills; ability to translate business requirements into technical solutions
- Experience mentoring or providing technical guidance to junior data scientists
- Master's or PhD in Computer Science, AI, Data Science, or related field
- Experience deploying GenAI solutions in a B2B enterprise or consulting environment
- Familiarity with vector databases (FAISS, Pinecone, Weaviate, Chroma) and hybrid search strategies
Fractal Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Fractal and has not been reviewed or approved by Fractal.
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Healthcare Strength — Health coverage includes medical, dental, and vision along with tax‑advantaged accounts and EAP in the U.S., indicating a broad core package. Feedback suggests core protections exist across regions, though specifics can vary by location.
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Leave & Time Off Breadth — Time‑off programs include generous PTO, paid holidays and sick time, paid volunteer time, and sabbaticals in some areas. Some accounts also describe manager‑approved or flexible PTO approaches alongside hybrid/WFH latitude.
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Flexible Benefits — Work arrangements commonly include remote/hybrid options and flexible schedules. Flexibility is frequently highlighted as part of the overall value proposition.
Fractal Insights
What We Do
Fractal is one of the most prominent players in the Artificial Intelligence space. Fractal's mission is to power every human decision in the enterprise and brings AI, engineering, and design to help the world's most admired Fortune 500® companies. Fractal's products include Qure.ai to assist radiologists in making better diagnostic decisions, Crux Intelligence to assists CEOs, and senior executives make better tactical and strategic decisions, Theremin.ai to improve investment decisions, and Eugenie.ai to find anomalies in high-velocity data & Samya.ai to drive next-generation Enterprise Revenue Growth Management. Fractal has more than 3,000 employees across 16 global locations, including the United States, UK, Ukraine, India, Singapore, and Australia. Fractal has consistently been rated as India's best companies to work for, by The Great Place to Work® Institute, featured as a leader in Customer Analytics Service Providers Wave™ 2021, Computer Vision Consultancies Wave™ 2020 & Specialized Insights Service Providers Wave™ 2020 by Forrester Research, and recognized as an "Honorable Vendor" in 2021 Magic Quadrant™ for data & analytics by Gartner.








