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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI/ML Lead/ Developer (Remote) - **Company:** Cognizant Technology Solutions Corporation - **Location:** Washington, DC, United States (Remote available) - **Experience:** Expert - **Salary:** $90,000.0 - $150,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Automated Storage and Retrieval Systems, Authentication Protocols, Microsoft Azure, Big Data, Cloud Computing, Cloud Engineering, Cluster Analysis, Continuous Integration, Information Engineering, Decision Support Systems, DevOps, Distributed Systems, Python (Programming Language), Machine Learning, Natural Language Processing, Object-Oriented Software Development, Performance Tuning, Backtesting, Azure Machine Learning, Azure Data Lake, Search Technologies, Software Construction, Software Engineering, Supervised Learning, Data Logging, Cloud Platform System, Feature Engineering, Chatbots, Large Language Models, Deep Learning, Model Validation, Generative AI, Backend, Fastapi, Build Management, Containerization, AI Platforms, Pyspark, Machine Learning Operations, Virtual Agents, Restful APIs, Data Pipelines, Api Management, Serverless Computing, Docker, Unsupervised Learning, Databricks, Microservices - **Published:** August 8, 2026 - **Apply:** https://dejobs.org/x/x/017688C2DB4C449FB8C3103A22028B7C/job/ ## About the Role * 8+ years of experience in Data Science, Machine Learning, AI Engineering, or related fields. * Expert-level proficiency in Python and PySpark for large-scale data processing and model development. * Strong experience with: * FastAPI * REST APIs * Microservices Architecture * Object-Oriented Programming * Software Engineering Best Practices * Hands-on experience with: * LangChain * LangGraph * RAG Architectures * Agentic AI Frameworks * LLM Application Development * Strong expertise in: * Azure Machine Learning * Azure OpenAI Service * Azure Databricks * Azure Data Lake * MLOps and CI/CD Practices * Experience developing and deploying enterprise-grade AI/ML solutions in cloud environments. Machine Learning & AI * Deep understanding of: * Supervised Learning * Unsupervised Learning * Deep Learning * Ensemble Methods * NLP * Time-Series Forecasting * Anomaly Detection * Risk Modeling * Strong understanding of model evaluation, feature engineering, experimentation, validation, and explainability. Domain Experience * Prior experience supporting: * Investment Banking * Capital Markets * Brokerage Operations * Trade Surveillance * Risk Management * Front Office or Middle Office Functions * Understanding of financial products, market data, and regulatory expectations is highly desirable. Soft Skills * Excellent communication and stakeholder management skills. * Ability to explain complex technical topics to non-technical audiences. * Strong analytical and problem-solving capabilities. * Experience working effectively within distributed and hybrid teams. Preferred Qualifications * Experience with vector databases such as Pinecone, Azure AI Search, Weaviate, or ChromaDB. * Knowledge of containerization technologies including Docker and Kubernetes. * Experience with CI/CD pipelines and DevOps practices. * Exposure to Responsible AI, Model Risk Management, and AI governance frameworks. * Azure certifications in AI, Data Science, or Machine Learning. ## Description We are seeking a highly skilled and innovative Senior AI/ML Engineer with strong expertise in Python, PySpark, Azure Machine Learning, Generative AI, and Full Stack Development to design and deliver advanced analytics and AI-driven solutions for global investment banking and brokerage operations. The ideal candidate will combine deep technical expertise in machine learning, distributed computing, cloud-native AI platforms, and modern AI frameworks such as LangChain, LangGraph, RAG, Agentic AI Frameworks, FastAPI, and Azure OpenAI Service . This role requires close collaboration with business stakeholders to transform complex financial data into actionable insights that improve decision-making, reduce operational risk, and enhance operational efficiency., Machine Learning & Advanced Analytics * Design, develop, and deploy advanced machine learning models using Python and PySpark to analyze large-scale financial datasets and generate actionable business insights. * Build predictive, classification, clustering, anomaly detection, forecasting, and risk models supporting investment banking and brokerage functions. * Perform rigorous model validation, back-testing, and experimentation using historical and simulated market data. * Evaluate and implement appropriate statistical, machine learning, deep learning, and AI techniques based on business requirements and regulatory considerations. * Optimize model performance through feature engineering, hyperparameter tuning, algorithm enhancements, and distributed computing techniques. Generative AI & Agentic Solutions * Design and implement enterprise-grade Generative AI solutions using Azure OpenAI Service . * Build and deploy Retrieval-Augmented Generation (RAG) applications leveraging vector databases and knowledge retrieval systems. * Develop intelligent agent-based systems using LangChain, LangGraph, and Agentic AI frameworks to automate business workflows and enhance decision support. * Apply Natural Language Processing (NLP), Large Language Models (LLMs), document intelligence, and conversational AI to streamline surveillance, reporting, compliance, and advisory functions. * Ensure safe, responsible, and governed adoption of Generative AI capabilities across the organization. Python Full Stack Development * Design and develop scalable backend services and APIs using FastAPI . * Build microservices and AI application frameworks that integrate machine learning and GenAI capabilities into enterprise ecosystems. * Develop reusable and maintainable software components following modern software engineering best practices. * Implement API integrations, authentication mechanisms, monitoring, logging, and performance optimization strategies. Data Engineering & MLOps * Design and implement scalable data pipelines and feature engineering workflows using Azure Machine Learning and cloud-native services. * Build reusable data products and machine learning components supporting multiple analytics and AI initiatives. * Partner with Data Engineering teams to operationalize machine learning models and AI applications. * Establish model monitoring, retraining strategies, experiment tracking, and lifecycle management processes. * Ensure solutions are secure, reliable, scalable, and production-ready. Cloud & Azure AI Platform * Develop end-to-end ML and AI solutions using: * Azure Machine Learning * Azure OpenAI Service * Azure Data Lake * Azure Databricks * Azure Storage Services * Azure DevOps * Manage model deployment, monitoring, governance, and operationalization on Azure platforms. * Support enterprise-scale AI and analytics workloads while maintaining compliance and security standards. Business Collaboration * Collaborate with product owners, business analysts, operations teams, and technology stakeholders to define high-value data science initiatives. * Translate complex investment banking and brokerage business challenges into measurable analytical solutions. * Present recommendations and analytical findings to both technical and non-technical audiences. * Drive adoption of AI and machine learning solutions through effective communication and stakeholder engagement. Governance & Responsible AI * Promote responsible AI practices by evaluating model fairness, explainability, bias, security, and data quality. * Document assumptions, risks, methodologies, and limitations in a transparent and accessible manner. * Ensure adherence to regulatory requirements, model governance frameworks, and enterprise AI policies. Leadership & Mentoring * Mentor junior data scientists, machine learning engineers, and developers. * Promote best practices in software development, experimentation, MLOps, AI engineering, and model governance. * Contribute to a culture of innovation, continuous learning, and technical excellence. ## Related Videos - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Inside the AI Revolution: How Microsoft is Empowering the World to Achieve More](https://www.wearedevelopers.com/videos/869-inside-the-ai-revolution-how-microsoft-is-empowering-the-world-to-achieve-more) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Got AI ideas but no money? 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