Role: GenAI Lead Engineer

Experience: 8+ years total (with significant hands-on GenAI / LLM work)

Job Overview:

We are seeking an innovative and highly skilled Lead Generative AI (GenAI) Engineer to spearhead the design, development, and deployment of advanced AI-powered solutions. In this role, you will lead a team of engineers and data scientists to harness cutting-edge Generative AI technologies and implement them to solve complex business problems, enhance user experiences, and drive innovation. This role combines deep technical expertise, leadership, and a strong understanding of AI trends and tools.

Key Responsibilities:

• Technical Leadership:
• Lead the end-to-end design and implementation of Generative AI solutions.
• Provide technical guidance and mentorship to engineers and data scientists working on GenAI projects.
• Stay updated with the latest trends, research, and advancements in Generative AI and Large Language Models (LLMs).
• Solution Development:
• Architect, train, and fine-tune state-of-the-art LLMs and generative AI models
• Develop and optimize pipelines for prompt engineering, retrieval-augmented generation (RAG), and domain-specific fine-tuning.
• Develop and deploy generative AI models, particularly focusing on ChatGPT, using Python on Azure or AWS Platform or .Net on Azure platform
• Ensure scalability, performance, and security of AI solutions deployed in production.
• Integration and Deployment:
• API Development: Ability to define and deliver API access for GenAI services, facilitating integration with other systems and applications.
• Collaborate with software engineering teams to integrate GenAI solutions into enterprise applications and services.
• Utilize cloud platforms (e.g., Azure, AWS, or GCP) to deploy and manage AI models and APIs.
• Leverage MLOps practices for continuous model monitoring, retraining, and improvement.
• Data Strategy and Preparation:
• Collaborate with data engineering teams to ensure high-quality data acquisition, preprocessing, and augmentation for model training and fine-tuning.
• Implement data governance and privacy practices in line with organizational policies.
• Innovation and Research:
• Experiment with new generative AI techniques, such as multimodal AI, reinforcement learning with human feedback (RLHF), and active learning.
• Evaluate and recommend AI frameworks, libraries, and platforms for project requirements.
• Stakeholder Collaboration:
• Work closely with product managers, business stakeholders, and UX designers to define AI-powered product features and use cases.
• Present technical concepts, project progress, and AI capabilities to non-technical audiences.

Key Requirements

• Technical Skills:
• Hands-on experience with cloud platforms and services for AI/ML, such as Azure AI Services, Azure Machine Learning, AWS Bedrock, or Google Vertex AI.
• Hands on experience in any of LLMs such as OpenAI’s ChatGPT Models , Gemini, Llama 2 ,Claude 2 ,Grok
• Hands on experience in any of the agentic frameworks like LangChain, Semantic kernel, AutoGen, CrewAi
• Hands on experience using any of vector database like Chroma, Pinecone, Weaviate, Faiss
• Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF)
• Strong expertise in LLMs and generative AI frameworks like OpenAI, Hugging Face Transformers, or similar platforms.
• Deep understanding of natural language processing (NLP) concepts, including tokenization, embeddings, and sequence-to-sequence models.
• Proficiency in Python and libraries such as TensorFlow, PyTorch, and Scikit-learn.
• Experience in CI/CD pipeline management and automation tools, particularly within the Azure DevOps environment. Knowledge of containerization (e.g., Docker) and orchestration tools is also important
• Familiarity with MLOps tools and practices, such as MLflow, Kubeflow, or Docker.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field (Ph.D. preferred).
• 8+ years of experience in AI/ML engineering, with 2+ years specifically in Generative AI.
• Minimum of 2 years of experience in building Conversational AI applications using cloud-based services and in orchestrating AI/ML services for building a complete solution
• Minimum of 6 years of extensive full-time experience in Data Analysis, Statistics, Machine Learning, or Computer Science
• Proven track record of leading AI projects from inception to production.
• Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF).
• Certifications in AI/ML or cloud platforms (e.g., Azure AI Engineer, AWS Certified Machine Learning).

The Role

We are looking for an elite engineer who builds like a hacker and communicates like a consultant. You will not just be building models in a notebook; you will be deployed to the front lines to build, architect, and ship end-to-end Agentic AI products directly for our most strategic customers.

This is a high-stakes role. You will face "tricky" customers who have vague requirements and high expectations. Your job is to translate their chaos into technical order, write production-grade code, and deploy autonomous agents that solve their actual business problems.

What You Will Do

• Build & Deploy (60%):

Design and ship full-stack AI applications. You own the pipeline from data ingestion to the LLM inference layer to the frontend interface.

Architect autonomous agentic workflows (using frameworks like LangChain, LangGraph, or custom implementations) that can reason, plan, and execute complex tasks.

Write robust, clean, and highly testable code (Python/Typescript). "It works on my machine" is not acceptable; it must work in the customer's restricted cloud environment.

Handle DevOps and MLOps constraints: Dockerizing agents, managing Kubernetes clusters, and optimizing inference latency.

• Customer Engineering (40%):

Serve as the technical face of the company for our most demanding accounts.

Navigate complex stakeholder environments. You will push back on unrealistic demands, clarify ambiguous requirements, and steer "tricky" customers toward technically feasible solutions without breaking rapport.

Translate technical constraints into business value for non-technical executives.

Generate high-quality data for our customers and ability to review the data generated by other experts. This is one of the most critical aspects of the role.

The "Must-Haves"

• Experience: 8–12 years of total engineering experience. You started as a strong Software Engineer and evolved into an AI specialist.
• Education: B.S./M.S. in Computer Science, Math, or Physics from a top-tier institution (IIT, MIT, Stanford, Harvard, Berkeley, CMU, or similar global top-ranking universities).
• Deep AI Fluency: You aren't just calling APIs. You understand RAG implementation nuances, vector database optimization, fine-tuning (LoRA/PEFT), and the architecture of agentic loops.
• Production Engineering: Experience with end-to-end deployment (AWS/GCP/Azure, CI/CD pipelines, Terraform, Docker).

The "Why You" (Soft Skills)

• Thick Skin & High EQ: You don't get flustered when a customer changes their mind for the third time in a week. You can hold your ground with a CTO and explain "why not" to a CEO.
• Extreme Attention to Detail: You catch the edge cases that others miss. You obsess over error handling, retry logic in agents, and data privacy.
• Grit: You enjoy the "messy" work of integrating legacy enterprise data just as much as prompting the latest model.

What will set you apart

• Public Code: A visible GitHub footprint is very helpful. We want to see your side projects, your contributions to open source, or your experiments with LLMs. Please include your GitHub link in your application.

Tech Stack

• Languages: Python (Expert).
• AI/Data (Expertise in all is not required): PyTorch, LangChain, LlamaIndex, Pinecone/Weaviate, OpenAI/Anthropic APIs, HuggingFace.
• Infra (Expertise in all is not required): Kubernetes, Terraform, AWS/GCP.

Role Overview

We are seeking a Principal Applied Scientist to be the highest-level AI technical authority on the team. This role is above the Applied Scientist level — you set the AI research and engineering direction, own the agentic system architecture, define the eval framework standards, and operate as the primary interface to the external AI research community and internal platform AI leadership. You will combine deep research rigor with relentless production focus: every AI capability you design ships, gets measured, and improves based on evidence.

You are the person who answers: "Is this AI approach the right one, and how do we know when it's working"

Key Responsibilities

• Own the agentic system architecture: define the multi-agent coordination patterns, MCP tool taxonomy, intent-to-skill routing logic, policy enforcement design, and memory management strategy across all AI workflows on the platform.
• Lead the AI evaluation framework: design the golden dataset structure, define eval metrics (RAG retrieval quality, scoring accuracy, LLM response correctness, agentic workflow success rate), and own the CI/CD AI eval pipeline that gates every AI capability release.
• Drive the RAG architecture strategy: chunking strategies for MongoDB and Elasticsearch-indexed documents, embedding model selection and fine-tuning, retrieval reranking design, and hallucination mitigation patterns.
• Own the LLM integration architecture: prompt engineering standards, LLM gateway usage policies, context window management, streaming response patterns, and model version governance.
• Lead applied research initiatives: identify novel techniques (RLHF, Constitutional AI, multi-modal, sparse retrieval) that are practically applicable to automotive retail intelligence use cases and prototype them rigorously in Python.
• Define the hexagonal AI adapter contract standard: the formal interface specification between Python AI services and Java domain cores — ensuring AI infrastructure remains swappable without domain logic changes.
• Act as primary technical interface to platform AI leadership (LLM Gateway, ML Platform, Agentic Engine) — influencing platform roadmap based on teams practical requirements.
• Mentor the Applied Scientist and ML Architect; conduct technical reviews of AI system designs, eval frameworks, and research approaches.
• Publish, present, or contribute to open-source where appropriate — representing Tekion's AI engineering excellence externally.

Skills & Qualifications

• 10+ years of applied AI/ML experience with at least 3 years at Staff or Principal level — a track record of shipping AI systems that measurably changed business outcomes.
• Deep expertise across: LLM fine-tuning and alignment, RAG system design (chunking, embedding, retrieval, reranking), agentic frameworks (LangGraph, CrewAI, AutoGen), and MCP tool design.
• Python mastery: PyTorch, Hugging Face Transformers, LangChain/LangGraph, scikit-learn, and production ML serving tooling.
• Strong understanding of distributed AI systems: Elasticsearch-backed vector search, Redis/Aerospike serving, Cosmos DB for AI event logging, and Kafka-based feature pipelines.
• Proven ability to design formal eval frameworks for AI systems — not just accuracy metrics but business aligned quality gates.
• Experience defining and enforcing hexagonal adapter contracts between AI services and domain service layers in production systems.
• Track record of communicating complex AI system design to non-technical stakeholders, influencing product roadmaps, and driving cross-team alignment. 

Good to Have

• Publications or significant open-source contributions in NLP, information retrieval, or agentic systems.
• Experience with Constitutional AI, RLAIF, or preference optimization techniques (DPO, PPO).
• Familiarity with multi-modal AI and its application to automotive retail data (images, documents, tabular data fusion).
• Prior experience in a product-focused AI role at a SaaS or platform company — not purely research. 

Perks & Benefits

• Competitive compensation and generous stock options.
• Medical insurance coverage.
• Work with some of the brightest minds from Silicon Valley's most dominant and successful companies.

Role: GenAI Lead Engineer

Experience: 8+ years total (with significant hands-on GenAI / LLM work)

Job Overview:

We are seeking an innovative and highly skilled Lead Generative AI (GenAI) Engineer to spearhead the design, development, and deployment of advanced AI-powered solutions. In this role, you will lead a team of engineers and data scientists to harness cutting-edge Generative AI technologies and implement them to solve complex business problems, enhance user experiences, and drive innovation. This role combines deep technical expertise, leadership, and a strong understanding of AI trends and tools.

Key Responsibilities:

• Technical Leadership:
• Lead the end-to-end design and implementation of Generative AI solutions.
• Provide technical guidance and mentorship to engineers and data scientists working on GenAI projects.
• Stay updated with the latest trends, research, and advancements in Generative AI and Large Language Models (LLMs).
• Solution Development:
• Architect, train, and fine-tune state-of-the-art LLMs and generative AI models
• Develop and optimize pipelines for prompt engineering, retrieval-augmented generation (RAG), and domain-specific fine-tuning.
• Develop and deploy generative AI models, particularly focusing on ChatGPT, using Python on Azure or AWS Platform or .Net on Azure platform
• Ensure scalability, performance, and security of AI solutions deployed in production.
• Integration and Deployment:
• API Development: Ability to define and deliver API access for GenAI services, facilitating integration with other systems and applications.
• Collaborate with software engineering teams to integrate GenAI solutions into enterprise applications and services.
• Utilize cloud platforms (e.g., Azure, AWS, or GCP) to deploy and manage AI models and APIs.
• Leverage MLOps practices for continuous model monitoring, retraining, and improvement.
• Data Strategy and Preparation:
• Collaborate with data engineering teams to ensure high-quality data acquisition, preprocessing, and augmentation for model training and fine-tuning.
• Implement data governance and privacy practices in line with organizational policies.
• Innovation and Research:
• Experiment with new generative AI techniques, such as multimodal AI, reinforcement learning with human feedback (RLHF), and active learning.
• Evaluate and recommend AI frameworks, libraries, and platforms for project requirements.
• Stakeholder Collaboration:
• Work closely with product managers, business stakeholders, and UX designers to define AI-powered product features and use cases.
• Present technical concepts, project progress, and AI capabilities to non-technical audiences.

Key Requirements

• Technical Skills:
• Hands-on experience with cloud platforms and services for AI/ML, such as Azure AI Services, Azure Machine Learning, AWS Bedrock, or Google Vertex AI.
• Hands on experience in any of LLMs such as OpenAI’s ChatGPT Models , Gemini, Llama 2 ,Claude 2 ,Grok
• Hands on experience in any of the agentic frameworks like LangChain, Semantic kernel, AutoGen, CrewAi
• Hands on experience using any of vector database like Chroma, Pinecone, Weaviate, Faiss
• Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF)
• Strong expertise in LLMs and generative AI frameworks like OpenAI, Hugging Face Transformers, or similar platforms.
• Deep understanding of natural language processing (NLP) concepts, including tokenization, embeddings, and sequence-to-sequence models.
• Proficiency in Python and libraries such as TensorFlow, PyTorch, and Scikit-learn.
• Experience in CI/CD pipeline management and automation tools, particularly within the Azure DevOps environment. Knowledge of containerization (e.g., Docker) and orchestration tools is also important
• Familiarity with MLOps tools and practices, such as MLflow, Kubeflow, or Docker.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field (Ph.D. preferred).
• 8+ years of experience in AI/ML engineering, with 2+ years specifically in Generative AI.
• Minimum of 2 years of experience in building Conversational AI applications using cloud-based services and in orchestrating AI/ML services for building a complete solution
• Minimum of 6 years of extensive full-time experience in Data Analysis, Statistics, Machine Learning, or Computer Science
• Proven track record of leading AI projects from inception to production.
• Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF).
• Certifications in AI/ML or cloud platforms (e.g., Azure AI Engineer, AWS Certified Machine Learning).

Cambridge Mobile Telematics (CMT) is the world’s largest telematics service provider. Its mission is to make the world’s roads and drivers safer. The company’s AI-driven platform, DriveWell® Fusion, gathers sensor data from millions of IoT devices — including smartphones, proprietary Tags, connected vehicles, dashcams, and third-party devices — and fuses them with contextual data to create a unified view of vehicle and driver behavior. Auto insurers, automakers, commercial mobility companies, and the public sector use insights from CMT’s platform to power risk assessment, safety, claims, and driver improvement programs. Headquartered in Cambridge, MA, with offices in Budapest, Chennai, Seattle, Tokyo, and Zagreb, CMT measures and protects tens of millions of drivers across the world every day.

We are embarking on a transformative journey with DriveWell Atlas, a groundbreaking initiative to build a family of novel AIs on telematics data. As a Senior Machine Learning Engineer, Foundation Models on the DriveWell Atlas team, you will help design and develop next-generation AI models that advance CMT’s capabilities in risk assessment, driver engagement, crash detection, and claims processing. This role offers the opportunity to work at the intersection of cutting-edge AI research and real-world telematics applications.

CMT is looking for a collaborative, customer-committed, and creative Senior Machine Learning Engineer, Foundation Models who wants to join us in making roads safer by making drivers better!

Responsibilities:

• Contribute to the design, training, and deployment of novel AI models for telematics applications
• Develop algorithms that model physical phenomena associated with vehicle and human movement
• Explore and implement advanced self-supervised learning methods tailored to multi-modal telematics sensor data
• Fine-tune upstream pretrained models or leverage their embeddings to solve downstream telematics tasks such as crash detection, driver risk scoring, and claims processing
• Build models robust to noise, missing data, and diverse real-world operating conditions
• Collaborate with engineers and product teams to integrate AI models into production systems
• Develop scalable training and inference pipelines using frameworks such as Ray, PyTorch DDP, or Horovod
• Optimize models for efficient deployment across cloud and edge/mobile environments
• Stay current with AI/ML research, applying new techniques to real-world telematics challenges
• Document and present findings, contributing to CMT’s AI/ML knowledge base
• Support junior team members through code reviews, collaboration, and knowledge sharing
• Complete any additional tasks as they arise

Qualifications:

• Bachelor’s degree or equivalent years of experience and/or certification in Artificial Intelligence, Computer Science, Electrical Engineering, Physics, Mathematics, Statistics, or a related field
• 4+ years of relevant experience in AI/ML (post-degree)
• Strong hands-on experience developing and training deep learning models, preferably transformers for time-series or multimodal data
• Familiarity with self-supervised learning and representation learning on noisy sensor data
• Proficiency in Python and data science libraries (e.g., Pandas, NumPy, scikit-learn)
• Strong experience with PyTorch (preferred) or TensorFlow for deep learning
• Practical experience with distributed training methods and efficient model training
• Experience with data processing pipelines and ML infrastructure (e.g., Spark, Airflow, Docker, AWS/GCP/Azure)
• Excellent problem-solving skills and ability to translate complex business problems into AI solutions
• Strong communication skills to effectively present technical concepts to diverse audiences
• Product-focused mindset with a track record of delivering impactful ML solutions

Nice to Haves:

• Master’s or PhD preferred
• Experience with model interpretability and explainability techniques (XAI)
• Familiarity with ethical AI principles, bias detection, and mitigation strategies
• Exposure to MLOps practices for managing the ML lifecycle
• Publications or open-source contributions in AI/ML

Compensation and Benefits:

• Fair and competitive salary based on skills and experience
• Equity may be awarded in the form of Restricted Stock Units (RSUs)
• Medical Benefits (Health insurance, Personal accident insurance, Group term life insurance), gratuity, parental leave, sick leave and public holidays
• Employees are eligible for flexible allowances which includes Leave Travel Assistance, Telephone/Mobile Expenses, Professional development expenses, Meals Coupon, Vehicle Reimbursement
• Flexible scheduling and work from home policy depending on role and responsibilities

Additional Perks:

• Feel great working to improve road safety around the world!
• Join one of our many employee resource groups including Black, AAPI, LGBTQIA+, Women, Book Club and Health & Wellness
• Extensive education and employee assistance programs
• CMT will do all that is possible to support our employees and create a positive and inclusive work environment for all!

Commitment to Diversity and Inclusion:

At CMT, we believe the best ideas come from a mix of backgrounds and perspectives. 

We are an equal-opportunity employer committed to creating a workplace and culture where everyone feels valued, respected, and empowered to bring their unique talents and perspectives. Diversity is essential to our success, and we actively seek candidates from all backgrounds to join our growing team. 

We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status or disability state. CMT is headquartered in Cambridge, MA. To learn more, visit www.cmtelematics.com and follow us on X @cmtelematics.