Role overview
Direct message the job poster from ThinkTrends
Jyotiska Biswas
Jyotiska Biswas
Code-less Generative AI for Enterprise
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.
What we're looking for
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
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 spaces.
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Seniority level
Mid-Senior level
Employment type
Full-time
Job function
Engineering and Information Technology
Industries
Software Development