Gen AI Machine Learning Engineer at bolttech

, , North Korea -

Full Time

Start Date

Immediate

Expiry Date

22 Jun, 26

Salary

0.0

Posted On

24 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Large Language Models, LLMs, Prompt Engineering, System Integration, Agents Orchestration, Langchain, AWS Bedrock, Generative AI, Langgraph, Mosaic AI Gateway, APIs, Agentic Workflows, RAG Systems, Vector Search, Python, NLP

Industry

Insurance

Description

In this position you will… Drive the development of custom Large Language Models (LLMs) across languages and modalities for bolttech AI-driven products. You will work on LLMs, prompt engineering, system integration, agents orchestration and more using frameworks such as Langchain, AWS Bedrock, or similar. This role offers an opportunity to innovate in an insurtech environment, delivering next generation Gen AI solutions that modernize how the business operates, working closely with the Head of AI, data scientists, engineers, and product manager. You will be responsible for… * Building and integrating generative AI applications for customer interactions by using LLMs and orchestration frameworks. * Designing and deploying custom solutions on AWS Bedrock by using foundation models from leading providers. * Creating and managing Chatbots and conversational workflows using tools such as Langgraph or similar. * Fine-tuning and evaluating large language models through Mosaic AI Gateway or designated computing resources and automated evaluation framework by using proprietary and external datasets. * Building scalable APIs and backend services to support real-time AI inference including but not limited to GenAI or Agentic AI based AI solution. * Architecting multi-stage agentic workflows and optimized RAG systems using the Mosaic AI Agent Framework and Vector Search. * Ensuring reliability, privacy, and accuracy of Gen AI responses by applying testing and monitoring tools. * Contributing to governance efforts by ensuring generative AI solutions follow responsible AI principles, including transparency, data privacy, and compliance with industry standards. * Working with the data science team to apply generative AI to other business areas, including document processing, claims decisioning, and reporting. * Documenting solutions and collaborating with engineering, product, and customer teams to align requirements and output. * Tracking industry trends and recommending tools or approaches to improve system performance and capability. For you to be successful… …we expect you to be able to demonstrate the following key competencies: Impactful * Proactive in identifying what needs to be done, and taking action, before being asked, or before the situation escalates. * Identifies, resolves problems, and removes obstacles that could hinder project’s success & progress. * Takes accountability and self-motivated to deliver results even in situations which are not straight-forward. Curious * Engages in learning activities and is interested in understanding different ways of doing things including the key activities performed by others in the team. * Keeps up to date on relevant frameworks, applications, software, and/or tools. Collaborative * Partners with a range of people to create trust and co-create and deliver mutually beneficial outcomes * Resolves disputes using a range of tactics to prioritize positive outcomes. * Collaborates in a thoughtful manner and values others’ unique contributions. Communication * Communicates clearly and actively listens to others. * Communication is specific and fact based using a style appropriate for the audience. * Receptive to feedback and appropriately expresses own opinions. Adaptable * Demonstrates flexibility in adapting to changing situations, tools and priorities. * Quickly learns and applies new tools, technologies and processes as needed. Literacy – Digital, Technology and Data * Proficient in navigating and using digital, data and technology tools and software applications relevant to role including performing basic troubleshooting strategies. * Uses appropriate data, technology, and digital solutions to communicate, solve problems, and access, manage, integrate, evaluate, and create information. * Able to understand, interpret, analyse, and communicate data and applies these skills to derive insights and performance. You will require the following qualifications and skills * 5+ years of experience in machine learning engineering, generative AI and LLM technologies, and Agentic workflows, or equivalent experience. * Experience with AWS Bedrock or similar. * Experience with LLM Agentic workflows and framework (Langchain, LangGraph, LlamaIndex etc.) * Knowledgeable in designing and implementing multi-layered AI safety architectures and adversarial red teaming strategies, utilizing frameworks such as Guardrails AI, or DeepTeam. * Advanced knowledge of Python and some experience in ML frameworks (PyTorch, JAX etc.) for training, fine-tuning, and deploying generative models is preferred. * Solid grasp of NLP techniques, multimodal AI (text, image, code), and agent workflows. * Knowledge and experience with transformer architectures, prompt engineering, retrieval-augmented generation (RAG), and LLM evaluation methodologies. * Knowledge of designing, implementing and managing distributed training pipelines for LLMs to ensure scalability and efficiency are nice to have. * Knowledge of Mosaic AI (Gateway, Training, Serving), MLflow (Tracing/Evaluate), Vector Search and Databricks Asset Bundles (DABs) are also nice to have. * Ability to contribute both independently and as part of a team. * Excellent analytical and problem-solving skills. * Excellent listening, communication, interpersonal and presentation skills to articulate complex technical ideas to cross-disciplinary internal and external stakeholders.

Responsibilities

The role involves driving the development of custom Large Language Models across languages and modalities for AI-driven products, focusing on LLMs, prompt engineering, and system integration using frameworks like Langchain and AWS Bedrock. Responsibilities include building and deploying generative AI applications, architecting agentic workflows, ensuring response reliability, and contributing to responsible AI governance.