Senior Data Scientist

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 seeking a highly capable Senior Data Science Engineer with strong foundations in Data Science, Machine Learning, Natural Language Processing, and Deep Learning, combined with hands-on expertise in LLM engineering and production software development.
• The ideal candidate must be able to independently design, architect, develop, optimize, deploy, and maintain production-grade AI/ML and Generative AI solutions. This role requires a combination of analytical depth, strong Python engineering, API development, and practical understanding of LLM behavior, tokenization, inference, evaluation, and performance optimization.
• We are looking for an engineer who can go beyond prototypes and framework integrations to understand the underlying technology, make sound architectural decisions, solve complex technical problems, and deliver reliable, measurable business outcomes.
  
  

Key Responsibilities

• Design and implement end-to-end AI/ML and Generative AI solutions, from problem formulation and data preparation through deployment and production monitoring.
• Build LLM-powered applications using commercial APIs and/or open-source models, selecting the appropriate approach based on quality, latency, cost, and operational requirements.
• Develop production-ready REST APIs and backend services using Python, FastAPI, or Flask, with appropriate validation, authentication, asynchronous execution, error handling, and testing.
• Apply strong ML, NLP, and Deep Learning fundamentals to model selection, experimentation, feature engineering, evaluation, and optimization.
• Implement LLM workflows involving prompt engineering, tokenization, embeddings, structured outputs, function calling, and context-window management.
• Design and optimize RAG pipelines, including document processing, chunking, retrieval, hybrid search, reranking, and grounded response generation.
• Optimize token consumption, context utilization, latency, throughput, and API/inference costs while maintaining acceptable output quality.
• Establish systematic evaluation and regression testing for model quality, retrieval relevance, groundedness, hallucinations, and application reliability.
• Implement robust production practices, including logging, monitoring, retries, timeouts, caching, versioning, and failure recovery.
• Independently troubleshoot complex issues and own the complete solution lifecycle, including architecture, development, deployment, and continuous improvement.
  
  

Mandatory Technical Requirements

• Python & Software Engineering: Strong Python programming, OOP, data structures, algorithms, debugging, testing, and production-quality coding.
• Data Science, ML, NLP & Deep Learning: Strong fundamentals in statistics, feature engineering, supervised/unsupervised learning, model evaluation, NLP, neural networks, transformers, and PyTorch/TensorFlow.
• GenAI & LLM Engineering: Hands-on experience building LLM-powered applications, prompt engineering, tokenization, embeddings, context management, structured outputs, function calling, and LLM evaluation.
• RAG & Vector Databases: Practical experience with RAG pipelines, document chunking, retrieval, reranking, and vector databases such as FAISS, Chroma, Pinecone, or PG Vector.
• API Development: Strong experience developing REST APIs using FastAPI or Flask, including asynchronous processing, validation, error handling, retries, timeouts, and testing.
• LLM Optimization: Experience optimizing token usage, latency, throughput, inference costs, and application performance through benchmarking and evaluation.
• LLM Frameworks & Fine-tuning: Hands-on experience with Hugging Face Transformers, LangChain/LlamaIndex, and fine-tuning techniques such as LoRA/QLoRA.
• Deployment & MLOps: Proven experience deploying and maintaining production AI/ML or GenAI applications, with knowledge of Docker, cloud platforms, CI/CD, monitoring, logging, and troubleshooting.
• End-to-End Ownership: Ability to independently design, develop, optimize, deploy, and maintain production-ready AI solutions.
  
  

What Success Looks Like

• The successful candidate can independently deliver a complete AI solution, justify model and architecture choices, demonstrate measurable improvements in quality or performance, and maintain the solution reliably in production.