Machine Learning Engineer – Generative AI
About ThinkTrendsThinkTrends is an Enterprise AI company building secure, intelligent automation solutions for regulated and enterprise environments. Our platform enables organizations to deploy generative and agentic AI capabilities with a focus on control, compliance, and transparency.We work with customers across the public sector and life sciences industry to modernize data workflows, streamline document processing, and deliver AI-driven decision support at scale. Our team brings together deep technical expertise and domain understanding to solve high-impact problems in complex, high-stakes environments.Role DescriptionThinkTrends is seeking a Machine Learning Engineer – Generative AI with 7+ years of experience and passion for Agentic AI systems to join our growing team. In this role, you'll design, build, and deploy intelligent agents that can reason, plan, and take autonomous actions to solve complex problems. This is an excellent opportunity for engineers looking to work at the cutting edge of AI agent development while growing their skills in a supportive environment.Key ResponsibilitiesAgentic AI DevelopmentDesign and implement AI agents using Large Language Models (LLMs)Build multi-agent systems that can collaborate, communicate, and coordinate to achieve goalsDevelop reasoning and planning capabilities for autonomous decision-makingCreate tool-using agents that can interact with APIs, databases, and external systemsImplement memory systems and context management for stateful agent interactionsModel Integration & DeploymentIntegrate various LLMs (GPT-4, Claude, Llama, etc.) into agent architecturesBuild and maintain agent orchestration pipelines and workflowsDeploy agents to production environments with appropriate safety guardrailsMonitor agent performance, behavior, and resource utilizationImplement feedback loops for continuous agent improvementCollaboration & LearningWork closely with senior engineers to design scalable agent architecturesParticipate in code reviews and contribute to best practicesDocument agent behaviors, architectures, and deployment processesStay current with rapidly evolving agentic AI research and frameworksContribute ideas for new agent capabilities and use casesRequired QualificationsEducation & ExperienceBachelor's degree in Computer Science, Engineering, Mathematics, or related field7+ years of professional experience in machine learning, software engineering, or related roleDemonstrated experience/interest in AI agents through projects, coursework, or professional workExperience building applications with LLMs or conversational AI systemsTechnical SkillsProgramming: Strong proficiency in Python; comfortable with async programmingLLM Integration: Hands-on experience with OpenAI API, Anthropic Claude, or similar LLM APIsCore ML: Understanding of machine learning fundamentals and model fine-tuningAPIs & Integration: Experience building and consuming RESTful APIsVersion Control: Proficient with Git and GitHub/GitLab workflowsDatabases: Working knowledge of SQL and vector databases (Pinecone, Weaviate, ChromaDB)Core CompetenciesUnderstanding of prompt engineering and LLM optimization techniquesFamiliarity with Retrieval-Augmented Generation (RAG) patternsBasic knowledge of agent architectures (ReAct, Chain-of-Thought, Tree of Thoughts)Problem-solving mindset with attention to detailStrong communication skills and ability to work collaborativelyEagerness to learn and adapt in a fast-moving fieldAwareness of AI safety, ethics, and responsible AI practicesPreferred QualificationsExperience with agent evaluation and benchmarkingKnowledge of reinforcement learning or RLHF conceptsFamiliarity with function calling and tool use in LLMsExperience with streaming responses and real-time agent interactionsUnderstanding of multi-modal agents (text, vision, audio)Contributions to open-source agent projects or frameworksExperience with cloud platforms (AWS, GCP, Azure)Knowledge of containerization (Docker) and orchestration (Kubernetes)Familiarity with observability tools for LLM applications (LangSmith, Weights & Biases)Technologies You'll Work WithLLM Providers: OpenAI, Anthropic, Google, open-source modelsVector Databases: Pinecone, Weaviate, ChromaDB, QdrantDevelopment Tools: Python, FastAPI, Git, Node.jsCloud Services: AWS/GCP/Azure (based on company infrastructure)Monitoring: LangSmith, Helicone, or similar LLM observability toolsCompensation & BenefitsCompetitive salary and benefits.Flexible hybrid work environment.Fast-paced, inclusive team culture focused on innovation and growth.Leadership opportunities in emerging AI and software innovation spaces.