Machine Learning Engineer (LLM)

DeepRec.ai; Boston (MA); Hybrid; USD 170,000 - 200,000; Full time

Job summary

A fast-growing AI company is seeking a Machine Learning Engineer to build and scale advanced systems powering multi-agent workflows. The role involves designing and productionizing large language models, optimizing their performance, and collaborating with cross-functional teams. Essential skills include proficiency in Python and ML frameworks, hands-on experience with LLMs, and familiarity with AWS. This is a full-time position based in Boston, offering flexibility for hybrid work.

Qualifications

• Strong proficiency in Python and ML frameworks like scikit-learn, TensorFlow, and PyTorch.
• Hands-on experience fine-tuning and training large language models (LLMs).
• Familiarity with AWS suite for deploying ML models.

Responsibilities

• Build, fine-tune, and productionize LLM pipelines.
• Develop APIs and data pipelines for AI conversations.
• Collaborate with data scientists and DevOps for scalable solutions.

Skills

Python

TensorFlow

PyTorch

Multi-agent orchestration

Conversational AI systems

Job description

Machine Learning Engineer (LLM)

Compensation: $170,000 - $200,000+ (DOE)
Location: Boston or Berkeley, flexible 2-3 days per week in office

We’re working a fast‑growing AI company on a mission to automate complex workflows in the financial services sector, starting with insurance. Their technology leverages cutting‑edge AI to simplify high‑value processes, from multi‑turn conversations to full workflow automation.

As an ML Engineer within LLMs, you’ll be building and scaling advanced AI systems that power intelligent, multi‑agent workflows. You’ll take ownership of designing, fine‑tuning, and productionizing large language models, integrating them with backend systems, and optimizing their performance. You’ll collaborate closely with data science, DevOps, and leadership to shape the AI infrastructure that drives the company’s automation solutions.

What You’ll Do

• Build, fine‑tune, and productionize large language model (LLM) pipelines, including PEFT, RLHF, and DPO workflows.
• Develop APIs, data pipelines, and orchestration systems for multi‑agent, multi‑turn AI conversations.
• Integrate models with backend services, including voice orchestration platforms and transcript generation.
• Optimize model usage and efficiency, transitioning from external APIs to in‑house solutions.
• Collaborate cross‑functionally with data scientists, DevOps, and leadership to deliver scalable machine learning solutions.

What We’re Looking For

Essential Skills & Experience

• Strong proficiency in Python and ML frameworks (e.g., scikit‑learn, TensorFlow, PyTorch).
• Hands‑on experience fine‑tuning and training LLMs.
• Experience with PEFT, DPO, Prefence Optimization, post‑training, supervised fine tuning, RLHF.
• Familiarity with AWS suite and deploying ML models to production.
• Ability to reason deeply about ML principles, architectures, and design choices.
• Knowledge of multi‑agent orchestration and conversational AI systems.

Desirable Skills & Experience

• Background in voice AI, speech‑to‑text, or text‑to‑speech systems.
• Exposure to financial services or insurance applications.
• Familiarity with optimizing models for long‑context scenarios.

For additional information or to apply, please get in touch or apply directly.

Seniority level

Not Applicable

Employment type

Full‑time

Job function

Information Technology