Forward Deployed Engineer (AI)

• Own the full lifecycle of AI/ML projects, including problem framing, data acquisition, feature engineering, model and LLM selection and fine-tuning, evaluation, deployment, monitoring, and continuous improvement.
• Design, build, and maintain scalable data pipelines for ingestion, preprocessing, and feature engineering, supporting both structured and unstructured enterprise data.
• Implement RAG pipelines using vector databases and embedding strategies to ground LLMs in proprietary enterprise data; fine-tune, prompt-engineer, and evaluate LLMs for domain-specific tasks; design and orchestrate agentic workflows including tool-using agents, multi-step planners, guardrails, and alignment mechanisms.
• Establish robust evaluation frameworks covering hallucination checks, calibration, bias and fairness, and adversarial testing; build logging, telemetry, safeguards, governance artifacts, and documentation for model risk management.
• Lead the end-to-end lifecycle of AI models from experimentation and prototyping to scalable deployment in production environments using MLOps best practices, CI/CD, and cloud platforms (AWS, Azure, GCP).
• Optimize inference latency, throughput, and cost; establish monitoring and observability to ensure performance, safety, and reliability in mission-critical environments.
• Work closely with AI engineers, software engineers, product managers, and business stakeholders to translate complex business problems into AI-native solutions with measurable impact.

• A bachelors' degree in a related field and at least 5 years of experience designing and deploying production-grade AI/ML solutions, including at least one production LLM agent or agentic workflow
• At least 5 years' experience working in Python and SQL, with hands-on experience in AI/ML frameworks such as scikit-learn, TensorFlow, or PyTorch
• Experience working in a consultative / client-facing position
• Proven experience with cloud-native development and data warehousing solutions (Snowflake, Azure Data Lake, AWS S3, or equivalent)
• Strong knowledge of LLMs, transformers, NLP, agentic modeling, and reinforcement learning concepts
• Experience with open and commercial LLMs, RAG pipelines, and agent frameworks (LangGraph, LangChain Agents, DSPy, or equivalent)
• Experience building data ETL/ELT pipelines and deploying models using Docker/Kubernetes, CI/CD, and monitoring/observability tools
• Familiarity with model risk management and trustworthy AI practices, especially in regulated industries
• Strong analytical, problem-solving, and communication skills; comfortable working across technical teams and business stakeholders

• You demonstrate tenured experience across data science, LLMs, and agentic AI. You can move fluidly between model experimentation and production engineering, and you understand the tradeoffs between approaches.
• You know how to establish and enforce evaluation frameworks to ensure production-ready deployment of AI/ML solutions, with particular sensitivity to the governance requirements of financial services and/or Insurance industries.
• You work effectively with onshore and offshore engineers, product managers, and business stakeholders.
• You can explain a RAG pipeline to a senior engineer and a hallucination risk to a compliance officer, adjusting your communication without dumbing things down.
• You can employ modern AI/ML and GenAI design patterns to solve complex business problems and integrate advanced AI into application architectures with measurable impact.
• You thrive in a fast-paced environment, stay current with a rapidly evolving AI landscape, and know when to adopt new tools and when to resist the hype.

Compensation and Perks:

As part of the Alpha team, you will be rewarded with the following: 

• Competitive salary with annual profit-sharing opportunity 
• 401k matching 
• 25 days of annual paid time off (accrued)
• Supplemented medical, dental and vision coverage 
• Reimbursement for commuting, mobile phone, and home internet expenses 
• North America team-wide training and retreats 
• Sponsorship towards professional certifications / training supported with 5 days of paid training time