Forward Deployed Engineer (AI)

Alpha Financial Markets Consulting

11h • $150,000 - $210,000 • Hybrid

About The Position

As a Sr. / Lead Forward Deployed Engineer at AlphaFMC, you will play a pivotal role in shaping how leading financial services organizations adopt and scale AI. You will work on high-visibility initiatives inside wealth management firms, banks, and insurance companies, where the stakes are high and the standards for performance, safety, and governance are non-negotiable. Your experience in data science, LLMs, and agentic AI will directly influence how our clients operate and compete. You will have real ownership across the full solution lifecycle, with visibility into both technical delivery and business outcomes, and meaningful input into the AI strategies we bring to market.

Requirements

• 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

Nice To Haves

• 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.

Responsibilities

• 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.

Benefits

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Number of Employees: 11-50 employees