Role Description We are seeking an experienced and talented Lead Machine Learning Engineer. Qualifications - Experience in machine learning and related technologies. - Strong programming skills in languages such as Python, R, or Java. - Proficiency in data analysis and statistical modeling. - Experience with machine learning frameworks and libraries. - Ability to work collaboratively in a team environment. Requirements - Proven experience in leading machine learning projects. - Strong understanding of algorithms and data structures. - Experience with cloud platforms (AWS, Google Cloud, Azure). - Excellent problem-solving skills and analytical thinking. - Strong communication skills, both verbal and written. Benefits - Flexible hours and remote-first mode. - Competitive compensation. - Complete Hardware/Software setup – anything you need for work. - Open-door culture, transparent communication, and top management at a handshake distance. - Health insurance, vacation, sick leaves, holidays, paid maternity/paternity leave. - Access to our learning & development center: workshops, webinars, training platform, and edutainment events. - Virtual team buildings and social activities. Company Description Innovecs is a global digital transformation tech company with a presence in the US, the UK, the EU, Israel, Australia, and Ukraine. Specializing in software solutions, the Innovecs team has experience in Supply Chain, Healthtech, Software & Hightech, and Gaming. - Included in the Inc. 5000 for the fifth year in a row. - Recognized in IAOP’s ranking of the best global outsourcing service providers. - Featured in the Global Top 100 Inspiring Workplaces Ranking. - Won gold at the Employer Brand Management Awards.

We’re looking for a driven, hands-on ML/AI Engineer to help us push the boundaries of what's possible in AI-driven drug development. You’ll work on production-grade LLM-based systems, knowledge graphs, and machine learning pipelines—turning prototypes into powerful tools that make a real-world impact. You'll collaborate across teams to design, build, and deploy intelligent systems that enhance how life sciences organizations make critical decisions. If you love working on technically challenging problems with direct impact in healthcare—this is the role for you. This is a remote-first position, but we’d love it if you’re based in or near Boston. What You'll Do: - Design and Deploy LLM Systems: Develop scalable, production-ready LLM applications using frameworks like LangChain/LangGraph. Build robust RAG pipelines and integrate knowledge graphs for biological and clinical data. - Full-Stack AI Engineering: Write maintainable, high-performance code and build clean APIs and services for machine learning applications. - Data Engineering Collaboration: Work with data engineers to build and optimize data workflows and pipelines for high-quality data ingestion and processing. - Product-Focused Prototyping: Collaborate with product and domain teams to rapidly prototype AI solutions, iterate based on feedback, and scale models for production. - Model Deployment & MLOps: Use modern MLOps tools to deploy and monitor models in production environments (AWS preferred). Ensure scalability, observability, and resilience. - Collaborative Innovation: Partner with engineering, data, and business teams to identify and develop high-value AI/ML applications. - Continuous Learning: Stay ahead of the curve on emerging ML frameworks, GenAI capabilities, and healthcare technologies. Requirements Core Qualifications: - Education: Bachelor's, Master’s, or Ph.D. in Computer Science, Data Science, Engineering, or a related field. - Hands-on AI Experience: Proven ability to build, train, and deploy ML and NLP models, especially those powered by LLMs and transformer architectures. - LLM & LangChain Experience: Practical experience working with frameworks like LangChain for applications such as Q&A systems, chatbots, or document automation. - Software Engineering: Strong coding skills in Python and experience using Git/GitHub and CI/CD practices. - Data Engineering Know-how: Comfort working with ETL pipelines, relational and non-relational databases, and data platforms like Snowflake or Databricks. - Big Data & ML Frameworks: Familiarity with Big Data tools (e.g., Apache Spark) and experience orchestrating data workflows using tools like Apache Airflow. - Cloud & MLOps: Experience with deploying ML models in cloud environments (AWS, GCP, or Azure) and using containerization/orchestration tools like Docker and Kubernetes. Soft Skills: - Strong problem-solving skills and an analytical mindset. - Passion for continuous learning, rapid prototyping, and iterating based on user needs. - Autonomous, self-starter attitude with a strong sense of ownership. - Excellent communication skills—able to explain technical ideas clearly to non-technical audiences. - Collaborative team player with a desire to build things that truly matter.

Role Description You will collaborate across product, engineering, and data teams to bring machine learning and Generative AI capabilities into production. Qualifications - Proven experience integrating Machine Learning models into customer-facing products - Deep understanding of MLOps, ML infrastructure, model lifecycle management, and observability - Strong programming skills in Python; additionally, experience with Go, Java, or similar languages will be a plus Requirements - Experience with Generative AI, LLMs, or applied AI product development - Experience with AWS services (e.g., EC2, EKS, RDS, ELB), infrastructure as code (Terraform), and CI/CD setup - Strong experience with LangChain/LangGraph framework - Strong experience with AWS Bedrock - Experience mentoring engineers and leading technical initiatives Benefits - Work on a new platform with little legacy dependencies for an organization putting AI and ML at the heart of its business. - Be a part of a highly technical team with the freedom to experiment and bring ideas to life. - Opportunity to work on both foundational ML infrastructure and applied Generative AI initiatives.

• Act as a solution expert across ML domains including evaluations, training, inference, data pipelines, labelling, and optimisation • Work directly alongside product teams as a trusted partner, helping them navigate technical challenges and arrive at effective solutions • Provide expert guidance on platform capabilities, helping teams understand what's possible and how to get there efficiently • Develop blueprints, patterns, and paved roads that allow other teams to follow proven approaches and accelerate their own implementations • Debug and resolve complex issues, identifying root causes and sharing learnings broadly • Balance hands-on problem solving with strategic thinking about how to scale enablement impact across the organisation