AI/ML Engineer
Syntas is seeking an experienced AI/ML Engineer to join our growing advisory practice. This is a unique opportunity to work at the intersection of cutting-edge AI technology and real-world business transformation, helping organizations across industries harness the power of large language models, intelligent automation, and production-grade AI systems.
About the Role
As an AI/ML Engineer at Syntas, you will be at the forefront of applied AI—not research for its own sake, but technology that ships, scales, and delivers measurable business outcomes. You will work directly with clients ranging from high-growth startups to established enterprises, understanding their challenges, architecting solutions, and building production systems that transform how they operate.
This is not a role where you build the same thing twice. Our advisory model means constant variety: one week you might be implementing a RAG-based knowledge system for a healthcare company, the next you are building AI-powered sales automation for a SaaS startup, and after that you are setting up LLM observability infrastructure for an enterprise client. You will become deeply proficient across the AI stack while developing the consulting skills to translate complex technology into business value.
We are a remote-first team that values autonomy, craftsmanship, and impact. You will have significant ownership over your projects, direct access to clients, and the freedom to explore new technologies and approaches. We invest heavily in our team—providing the latest tools, generous learning budgets, and opportunities to attend and speak at conferences.
The ideal candidate combines strong software engineering fundamentals with hands-on LLM experience and the communication skills to work effectively with non-technical stakeholders. You should be comfortable context-switching between projects, enjoy the challenge of learning new domains quickly, and take pride in building systems that actually work in production—not just demos that impress in a pitch deck.
What You Will Build
- 1Production RAG systems that retrieve and synthesize information from large document corpora with high accuracy and low latency
- 2Multi-agent architectures with specialized subagents, handoffs, routers, and human-in-the-loop workflows
- 3AI-powered automation pipelines for sales, operations, and customer service that integrate with existing business systems
- 4LLM observability and evaluation frameworks using tools like Langfuse for tracing, debugging, and continuous improvement
- 5Custom fine-tuned models for domain-specific applications where off-the-shelf models fall short
- 6Voice AI systems and conversational interfaces that handle real-world complexity gracefully
- 7Internal tooling and frameworks that accelerate AI development across client engagements
Key Responsibilities
- Design, build, and deploy production AI/ML solutions for client engagements across healthcare, finance, logistics, SaaS, and other industries
- Architect and implement LLM-based applications including chatbots, agents, automated workflows, and intelligent document processing systems
- Build and optimize RAG pipelines with vector databases (Pinecone, Weaviate, Chroma, pgvector), embedding models, and retrieval strategies
- Develop robust integrations with AI APIs (OpenAI, Anthropic, Cohere) and open-source models (Llama, Mistral, etc.)
- Implement comprehensive observability using Langfuse, LangSmith, or similar tools—including tracing, evaluation, A/B testing, and prompt versioning
- Create evaluation frameworks combining deterministic testing, LLM-as-judge approaches, and human feedback loops
- Design multi-agent systems with appropriate state management, memory (short-term and long-term), and error handling
- Deploy and manage AI infrastructure on cloud platforms (AWS, GCP, Azure) with proper CI/CD, monitoring, and scaling
- Collaborate directly with clients to gather requirements, present solutions, and iterate based on feedback
- Optimize prompts, embeddings, and retrieval mechanisms through systematic experimentation and measurement
- Contribute to internal knowledge bases, best practices documentation, and reusable components
- Stay current with rapidly evolving AI/ML landscape and evaluate new tools, models, and techniques for client applications
- Mentor junior team members and contribute to a culture of technical excellence
What We Are Looking For
- 4+ years of software engineering experience with at least 2 years focused on AI/ML or LLM-based systems
- Strong Python proficiency including modern tooling (Poetry/uv, Pydantic, async patterns, type hints)
- Production experience building and deploying LLM applications—not just prototypes or demos
- Deep understanding of RAG architectures including chunking strategies, embedding models, vector stores, and retrieval optimization
- Hands-on experience with LangChain, LlamaIndex, or similar orchestration frameworks
- Familiarity with prompt engineering techniques, including few-shot learning, chain-of-thought, and structured outputs
- Experience with at least one major cloud platform (AWS, GCP, or Azure) including serverless, containers, and managed services
- Understanding of MLOps practices including CI/CD for ML, model versioning, and monitoring
- Strong debugging and problem-solving skills—ability to trace issues through complex distributed systems
- Excellent written and verbal communication skills with experience explaining technical concepts to non-technical audiences
- Self-directed work style with ability to manage multiple projects and priorities in a remote environment
- Consultative mindset—you enjoy understanding client problems deeply and proposing creative solutions
Nice to Have
- Experience with agentic frameworks like LangGraph, CrewAI, or AutoGen for building multi-agent systems
- Background in model fine-tuning (LoRA, QLoRA, full fine-tuning) and when to use it vs. prompting vs. RAG
- Experience with AI observability tools (Langfuse, LangSmith, Helicone, Weights & Biases)
- Familiarity with local/self-hosted model deployment (Ollama, vLLM, TGI, llama.cpp)
- Knowledge of voice AI and speech-to-text/text-to-speech systems
- Experience with TypeScript/Node.js in addition to Python
- Background in specific verticals we serve: healthcare, finance, logistics, legal, or SaaS
- Prior consulting, agency, or client-facing technical role experience
- Contributions to open-source AI/ML projects
- Experience with data engineering and ETL pipelines
- Familiarity with traditional ML in addition to LLMs (classification, regression, clustering)
- Public speaking, technical writing, or content creation experience
Tech Stack
Benefits & Perks
- Competitive salary: $140,000 - $200,000 depending on experience
- Equity participation with meaningful upside as we grow
- Fully remote work with flexible hours—work from anywhere in the US
- Comprehensive health, dental, and vision insurance (100% premium covered for employee)
- Unlimited PTO with encouraged minimum of 4 weeks—we mean it
- $3,000 annual learning and development budget for courses, books, and certifications
- Conference attendance budget including travel—attend or speak at AI conferences
- Top-tier hardware: MacBook Pro, external display, and peripherals of your choice
- All AI tools and subscriptions you need: GPT-4, Claude, GitHub Copilot, and more
- Quarterly team offsites in interesting locations
- 401(k) with company match
- Paid parental leave (12 weeks)
- Home office setup stipend ($1,000)
- Work on genuinely interesting problems across diverse industries
Ready to Apply?
Send us your resume and a brief introduction. Tell us about your experience with AI/ML systems and what excites you about this opportunity.