Senior Machine Learning Engineer

Job description

About ThinkTrends

ThinkTrends 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.

Position Overview

ThinkTrends is seeking a Senior Machine Learning Engineer with 6+ years of professional experience in machine learning, AI engineering, or software engineering, including demonstrated experience building and deploying ML systems in production environments.

This role requires ownership of architecture, system reliability, and production scalability. The selected candidate will design, build, and operationalize intelligent agentic AI systems that reason, plan, use tools, and operate safely within regulated enterprise environments.

The position focuses on delivering robust, production-grade AI systems, not experimentation alone.

Key Responsibilities

Agentic AI Architecture & Development

• Architect and implement scalable AI agents using Large Language Models (LLMs)
• Design multi-agent systems with structured coordination and delegation mechanisms
• Implement reasoning, planning, and tool-use frameworks for autonomous systems
• Design and manage short-term and long-term memory systems
• Define and implement guardrails, validation layers, and policy enforcement mechanisms
• Evaluate and select agent orchestration strategies and frameworks

Model Integration & Production Deployment

• Integrate multiple LLM providers (OpenAI, Anthropic, Google, open-source models)
• Design and maintain Retrieval-Augmented Generation (RAG) pipelines
• Build structured function-calling systems and API-integrated agents
• Deploy ML and agent systems to production with monitoring, logging, and safety controls
• Establish evaluation benchmarks and regression testing workflows
• Monitor latency, cost, hallucination patterns, and system performance

Infrastructure & MLOps

• Develop CI/CD pipelines for ML and LLM-based systems
• Containerize services using Docker
• Collaborate on Kubernetes or equivalent orchestration environments
• Ensure observability, traceability, and operational resilience
• Optimize cloud infrastructure for performance and cost

Technical Leadership

• Lead architecture discussions and system design decisions
• Mentor junior engineers and review code
• Translate enterprise requirements into scalable AI solutions
• Contribute to engineering standards and documentation

Required Qualifications

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field
• 6+ years of professional experience in ML, AI engineering, or software engineering
• Demonstrated experience deploying ML systems in production environments
• Proven experience building and operationalizing LLM-based systems or AI agents
• Experience working in enterprise or regulated environments preferred

Technical Skills

Programming & Engineering

• Strong Python proficiency (including async programming)
• Experience building production APIs (FastAPI or similar)
• Solid software engineering practices (testing, modularity, version control)

LLM & Agent Systems

• Experience integrating commercial and open-source LLMs
• Strong understanding of RAG architectures and vector search systems
• Experience implementing structured function-calling and tool-use agents
• Familiarity with advanced agent reasoning paradigms (ReAct, multi-agent orchestration)

Data & Infrastructure

• SQL proficiency
• Experience with vector databases (Pinecone, Weaviate, Qdrant, ChromaDB)
• Docker experience
• Familiarity with Kubernetes or orchestration platforms
• Cloud experience (AWS, Azure, or GCP)

Observability & Evaluation

• Experience evaluating and benchmarking LLM-based systems
• Familiarity with monitoring tools (LangSmith, Weights & Biases, Helicone or similar)
• Understanding of AI safety, hallucination mitigation, and governance mechanisms

Preferred Qualifications

• Experience building AI systems for government or life sciences sectors
• Experience designing AI systems under compliance constraints
• Knowledge of reinforcement learning or RLHF concepts
• Experience with multi-modal agents
• Contributions to open-source AI or agent frameworks

Technologies You'll Work With

• LLM Providers: OpenAI, Anthropic, Google, open-source models
• Vector Databases: Pinecone, Weaviate, ChromaDB, Qdrant
• Development Tools: Python, FastAPI, Git, Node.js
• Cloud Services: AWS / Azure / GCP (based on company infrastructure)
• Monitoring: LangSmith, Helicone, or similar LLM observability tools

Compensation & Benefits

• Competitive 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