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 Machine Learning Engineer with 6+ 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 Responsibilities

Agentic AI Development

Design and implement AI agents using Large Language Models (LLMs)
Build multi-agent systems that can collaborate, communicate, and coordinate to achieve goals
Develop reasoning and planning capabilities for autonomous decision-making
Create tool-using agents that can interact with APIs, databases, and external systems
Implement memory systems and context management for stateful agent interactions

Model Integration & Deployment

Integrate various LLMs (GPT-4, Claude, Llama, etc.) into agent architectures
Build and maintain agent orchestration pipelines and workflows
Deploy agents to production environments with appropriate safety guardrails
Monitor agent performance, behavior, and resource utilization
Implement feedback loops for continuous agent improvement

Collaboration & Learning

Work closely with senior engineers to design scalable agent architectures
Participate in code reviews and contribute to best practices
Document agent behaviors, architectures, and deployment processes
Stay current with rapidly evolving agentic AI research and frameworks
Contribute ideas for new agent capabilities and use cases

Required Qualifications

Education & Experience

Bachelor's degree in Computer Science, Engineering, Mathematics, or related field
6 years of professional experience in machine learning, software engineering, or related role
Demonstrated experience/interest in AI agents through projects, coursework, or professional work
Experience building applications with LLMs or conversational AI systems

Technical Skills

Programming: Strong proficiency in Python; comfortable with async programming
LLM Integration: Hands-on experience with OpenAI API, Anthropic Claude, or similar LLM APIs
Core ML: Understanding of machine learning fundamentals and model fine-tuning
APIs & Integration: Experience building and consuming RESTful APIs
Version Control: Proficient with Git and GitHub/GitLab workflows
Databases: Working knowledge of SQL and vector databases (Pinecone, Weaviate, ChromaDB)

Core Competencies

Understanding of prompt engineering and LLM optimization techniques
Familiarity with Retrieval-Augmented Generation (RAG) patterns
Basic knowledge of agent architectures (ReAct, Chain-of-Thought, Tree of Thoughts)
Problem-solving mindset with attention to detail
Strong communication skills and ability to work collaboratively
Eagerness to learn and adapt in a fast-moving field
Awareness of AI safety, ethics, and responsible AI practices

Preferred Qualifications

Experience with agent evaluation and benchmarking
Knowledge of reinforcement learning or RLHF concepts
Familiarity with function calling and tool use in LLMs
Experience with streaming responses and real-time agent interactions
Understanding of multi-modal agents (text, vision, audio)
Contributions to open-source agent projects or frameworks
Experience 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 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/GCP/Azure (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 spaces.

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Machine Learning Engineer