Job description

About Us:

Solytics Partners is a Global Analytics firm, recognized with multiple industry awards for innovation and excellence. Our team comprises experts with deep domain knowledge in risk, analytics, AI/ML, AML/FCC, and fraud. By converging this expertise with cutting-edge technologies like AI, Machine Learning, Generative AI, and Large Language Models (LLMs), we deliver powerful automated platforms and incisive point solutions.

Our offerings enable clients to streamline and future-proof their risk, AML, and analytics processes, comply seamlessly with global regulations, and safeguard financial systems. Whether it’s solving complex challenges or driving operational efficiency, Solytics Partners is committed to empowering organizations with transformative tools to stay ahead in an evolving regulatory landscape.

Job Summary

We are looking for a Senior AI Engineer to join our team and take full ownership of AI-powered systems: from requirements to production. You are an engineer first: you write clean, testable Python, think critically about when AI is and isn't the right tool, and you know how to ship. You have hands-on experience with modern foundation models and AI vendors (OpenAI, Anthropic, Gemini, Mistral, etc.), and you understand the craft of steering LLM behavior: prompt design, context management, output validation, and guardrails. If you thrive in environments where ownership is real and the problems are hard, this role is for you.

Key Responsibilities

Own AI features end-to-end: engage with stakeholders to clarify requirements, design the solution, implement it in Python, and drive it through to a stable production deployment.
Architect and build LLM-powered systems including RAG pipelines, agentic workflows, multi-model orchestration, and embedding-based search; choosing the right approach for each use case rather than defaulting to the trendiest one.
Engineer LLM interactions with rigor: design system prompts, manage context windows, implement output parsing, and apply guardrails (content safety, hallucination mitigation, schema enforcement) to make model behavior predictable and production-safe.
Make sound "use LLM / don't use LLM" decisions: know when a classical ML model, a rules engine, or a simple heuristic is a better fit, and be able to justify that call clearly.
Build and maintain CI/CD pipelines for AI workloads, including automated testing, model evaluation gates, and continuous deployment to staging and production environments.
Instrument AI systems for observability: monitor latency, token costs, error rates, and model drift; set up alerting and feedback loops to catch regressions early.

Key Requirements

Machine Learning & AI

4+ years of industry experience in ML, AI or Data Science role.
Strong foundation in ML concepts: supervised/unsupervised learning, model evaluation, bias-variance trade-off, and when classical ML outperforms generative approaches.
Proven, hands-on experience with foundation models and LLM APIs: OpenAI, Anthropic Claude, Google Gemini, Mistral, or open-source equivalents.
Deep practical knowledge of prompt engineering and structured output techniques.
Familiarity with RAG architectures, vector databases and orchestration frameworks such as LangChain or LlamaIndex.

Engineering & Python

Expert-level Python: clean, well-tested, production-grade code.
Strong grasp of software engineering fundamentals: data structures, algorithms, system design, REST API design.

Mindset

Strong critical thinking and intellectual honesty: able to push back on overcomplicated solutions and advocate for the simplest thing that works.
Comfortable with ambiguity; able to define scope, make trade-off decisions, and move forward without needing every detail pre-specified.
Clear communicator who can translate technical findings into business impact and vice versa.

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