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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. AI/ML Engineer - **Company:** Synechron Inc - **Location:** Des Plaines, IL, United States - **Experience:** Expert - **Salary:** $120,000.0 - $160,000.0 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Java (Programming Language), .NET Framework, Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Application Testing, C Sharp (Programming Language), Cloud Computing, Code Review, Encodings, Continuous Integration, Data Cleansing, Information Engineering, Data Governance, Data Integration, Software Debugging, Github, Information Extraction, Python (Programming Language), Machine Learning, Productivity Software, OpenAI, Cloud Services, Standard Sql, Search Technologies, Software Engineering, SQL Databases, Data Streaming, Enterprise Data Management, Data Logging, Data Ingestion, GitHub Copilot, LangChain, ReactJS, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Model Validation, Generative AI, Agentic-AI, Rate Limiting, Git, AI Platforms, Pyspark, Kubernetes, Low Latency, Machine Learning Operations, Claude, Invoking Functions, Restful APIs, Model Context Protocol, Prompt Templates, Semantic Kernel, GPT, Software Version Control, Data Pipelines, Programming Languages - **Published:** October 2, 2026 - **Apply:** https://www.disabledperson.com/jobs/75663613-sr-ai-ml-engineer ## About the Role AI, Machine Learning, and Generative AI * Hands-on experience building and deploying LLM and GenAI applications in production. * Strong understanding of AI/ML concepts, frameworks, model lifecycle management, and AI application architecture. * Experience with production AI patterns such as: * RAG * Agentic AI * MCP * NLP-to-SQL * Semantic search * Document intelligence * Tool and function calling * Proficiency in generative AI and LLM data preparation for financial services use cases. * Experience with prompt design, prompt evaluation, context management, and output validation. * Working knowledge of at least one major model provider API, such as: * OpenAI GPT * Anthropic Claude * Experience implementing streaming, function calling, tool integration, and model orchestration. Software Engineering and Development * Strong proficiency in Python and SQL. * Experience using VS Code and developer productivity tools such as GitHub Copilot. * Experience with one or more of the following programming languages and technologies: * .NET / C# * Java * React * Strong understanding of software development practices, version control, testing, debugging, code review, and CI/CD. * Proficiency with Git and GitHub-based development workflows. * Experience designing and consuming RESTful APIs. * Understanding of API authentication, authorization, rate limiting, monitoring, logging, and error handling. Preferred skills: Experience developing enterprise-grade GenAI platforms or AI assistants. Knowledge of MLOps, LLMOps, model evaluation, observability, and responsible AI practices. Experience with orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, or comparable technologies. Knowledge of embedding models, reranking, chunking strategies, vector indexing, and retrieval evaluation. Experience with secure handling of sensitive, confidential, and regulated financial data. Familiarity with cloud AI services and managed model platforms. Experience with Agile delivery methodologies and cross-functional product development. ## Description We are seeking an experienced AI/ML Engineer to design, develop, and deploy production-grade AI and generative AI solutions for financial services and asset management use cases. The ideal candidate will have strong hands-on experience building LLM-powered applications, data integration platforms, retrieval-augmented generation systems, agentic AI workflows, and NLP-to-SQL solutions. This role requires a combination of software engineering, data engineering, machine learning, cloud, and financial domain expertise. The successful candidate will work closely with product teams, data engineers, business stakeholders, risk teams, and technology leaders to deliver secure, scalable, governed, and compliant AI solutions., The base salary for this position will vary based on geography and other factors. In accordance with law, the base salary for this role if filled within Dallas, TX is $120K - $160K & benefits (see below). The Role Responsibilities: Design, build, and deploy production-grade AI/ML and generative AI applications. Develop LLM-based solutions using patterns such as: * Retrieval-Augmented Generation (RAG) * Agentic AI * Model Context Protocol (MCP) * NLP-to-SQL * Function and tool calling * Streaming responses * Intelligent document and financial data processing Prepare, cleanse, transform, and curate data for LLM and generative AI use cases. Design and implement data ingestion and integration pipelines using SQL, PySpark, cloud services, and enterprise data platforms. Build scalable services and APIs for AI/ML applications. Design, consume, and integrate REST APIs with appropriate authentication, rate limiting, validation, monitoring, and error handling. Work with vector databases and embedding technologies to implement semantic search and knowledge retrieval solutions. Integrate AI applications with structured and unstructured enterprise data sources. Develop secure and reusable prompt templates, system instructions, evaluation frameworks, and model interaction patterns. Work with major model provider APIs, including OpenAI GPT and/or Anthropic Claude. Implement model features such as prompt engineering, function calling, tool use, streaming, context management, and response validation. Deploy AI solutions using cloud platforms, containerized architectures, and modern CI/CD practices. Collaborate with data scientists, software engineers, product managers, and business stakeholders to translate requirements into technical solutions. Ensure solutions comply with data governance, security, regulatory, privacy, and risk-management requirements. Conduct model and application testing, including quality, accuracy, performance, security, and responsible AI assessments. Monitor production AI applications and continuously improve reliability, cost efficiency, latency, and output quality. 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