• Design, build, and deploy online machine learning models to catch evolving fraud vectors in real time. • Engineer high-frequency time-series features from over 1 trillion behavioral events, optimizing for low-latency signal extraction and pattern recognition. • Maintain and enhance our automated model training and deployment infrastructure, ensuring frictionless CI/CD of newly trained models. • Write high-performance code to minimize scoring latency at runtime, ensuring our core ML services scale seamlessly across distributed databases. • Work cross-functionally with Core Infrastructure, Product Management, and Data Science teams to translate business-level fraud patterns into robust algorithmic solutions.

Role Description Think of TEKsystems Global Services (TGS) as the growth solution for enterprises today. We unleash growth through technology, strategy, design, execution and operations with a customer-first mindset for bold business leaders. We deliver cloud, data and customer experience solutions. Here’s what the opportunity supported through our TGS Talent Acquisition Team requires: Key Responsibilities - Generative AI & Agent Development - Design and implement scalable full-stack applications integrating advanced AI capabilities and autonomous agent systems. - Develop and maintain sophisticated agentic AI solutions including autonomous agents, multi-agent systems, and AI orchestration workflows. - Build intelligent agents capable of reasoning, planning, decision-making, and autonomous task execution. - Implement agent communication protocols and coordination mechanisms for complex multi-agent scenarios. - Design and optimize AI workflows using agent frameworks such as Google ADK, A2A, AutoGen, CrewAI, Lang Graph, LangFlow, Semantic Kernel, and OpenAI Agent SDK. - Technical Architecture & Integration - Architect and develop robust frontend interfaces and backend services for AI-driven platforms using modern frameworks. - Integrate multiple Large Language Models (LLMs) including GPT-4, Claude, Gemini, and open-source models like Llama 3, Mistral, CodeLlama, and Vicuna. - Implement and optimize AI orchestration frameworks including LangChain, LlamaIndex etc. - Design Model Context Protocol (MCP) implementations for seamless model interoperability. - Develop custom agent frameworks and extend existing platforms like Microsoft AI Agent Development Kit (ADK) and Google AI Platform. - DevOps & Production Systems - Implement comprehensive AI observability and monitoring using Lang Fuse, Pheonix, Datadog or Dynatrace. - Deploy and manage AI applications using containerization (Docker, Kubernetes) and cloud platforms (AWS, GCP, Azure). - Establish CI/CD pipelines for AI model deployment, version control, and automated testing. - Implement prompt engineering best practices, A/B testing frameworks for AI responses, and performance optimization. - Monitor model performance, drift detection, and implement feedback loops for continuous improvement. - Collaboration & Quality Assurance - Collaborate with research, product, and data science teams to prototype and deploy production ready intelligent systems. - Ensure scalability, reliability, security, and ethical considerations in the deployment of agentic AI systems. - Participate in code reviews, testing, documentation, and knowledge sharing to ensure high-quality software delivery. - Mentor junior developers and contribute to technical decision-making processes. Qualifications - Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, or related technical field. - 5+ years of experience in full-stack development with proficiency in modern frameworks and programming languages. - 3+ years of hands-on experience building AI-powered applications and autonomous agent systems. Requirements - Technical Expertise - Programming Languages: Proficiency in Python, TypeScript/JavaScript, with experience in Rust, Go, or Java preferred. - Frontend Frameworks: React, Vue.js, Angular, Next.js, or similar modern frameworks. - Backend Technologies: Node.js, FastAPI, Django, Express.js, microservices architecture. - Agent Frameworks: Hands-on experience with LangChain, AutoGen, CrewAI, LangGraph, OpenAI Assistants API, or Microsoft ADK. - LLM Integration: Proven experience integrating and optimizing multiple language models (GPT, Claude, Gemini, open-source models). - AI/ML Fundamentals: Strong understanding of transformer architectures, prompt engineering, embeddings, vector databases, and RAG systems. - Specialized AI Knowledge - Proven experience in building autonomous agents, multi-agent systems, and agent orchestration platforms. - Strong understanding of agent-based modeling, reinforcement learning, AI planning techniques, and decision-making algorithms. - Experience with Model Context Protocols (MCP) and multi-model integration patterns. - Knowledge of AI safety, alignment, and ethical AI deployment practices. - Familiarity with vector databases (Pinecone, Weaviate, Chroma) and semantic search implementations. Benefits - Medical, Dental, and Vision. - Critical Illness, Accident, and Hospital. - 401(k) Retirement Plan – Pre-tax and Roth post-tax contributions available. - Life Insurance (Voluntary Life and AD&D for employee and dependents). - Short and Long-Term Disability. - Health Spending Account (HSA). - Transportation Benefits. - Employee Assistance Program. - Time Off/Leave (PTO, Vacation or Sick Leave). Company Description We're partners in transformation. We help clients activate ideas and solutions to take advantage of a new world of opportunity. We are a team of 80,000 strong, working with over 6,000 clients, including 80% of the Fortune 500, across North America, Europe and Asia.

Role Description Joining us as a Machine Learning Engineer, you'll play a key role in one of the aspects of developing software and tech for improving map features, navigation, and high-definition maps. You'll bring your experience and skills to our exciting project within a competent, cross-functional, passionate, and self-organized team. In this role, you can expect to: - Take ownership of ML projects across the company; - Design and implement new feature extraction pipelines from open source and proprietary data; - Collect and monitor technical and business metrics; - Assume a key position in deliberating and implementing security best practices. Qualifications - 5+ years of hands-on experience with Machine Learning / Data Science; - Good knowledge of Python (or any other programming language); - Proficient in SQL operations; - End-to-end solution support; - Basic understanding of distributed computing principles; - Creative, resourceful and innovative problem solver; - Good communication skills in English, both written and spoken. Requirements - Hands-on experience with Spark / PySpark / MapReduce; - Deep Learning / LMM will be a plus; - Experience with Hadoop (or similar) Ecosystem; - Experience with workflow management tools (Airflow / Oozie / Luigi / Prefect); - Experience with AWS services, in particular S3, EC2, IAM, EMR, Glue, Athena, Lambda. Benefits - Supportive health care; - Parental leave; - Flexibility for personal matters; - Innovative support for employees. Company Description Mapbox is the leading real-time location platform for a new generation of location-aware businesses. Mapbox is the only platform that equips organizations with the full set of tools to power the navigation of people, packages, and vehicles everywhere. More than 4 million registered developers have chosen Mapbox because of the platform’s flexibility, security and privacy compliance. On the HD Maps team, we are at the forefront of geospatial big-data analytics and insights for customer market segments and product offerings. Our expertise is pivotal in deploying GIS algorithmic stages into scalable production cloud applications, leveraging platforms like AWS and Spark. We work on Mapbox's award-winning high-precision maps (“HD Maps”) products family, spanning across ADAS, AV, and Non-Automotive GIS data customers in numerous projects. We cover everything from data and systems analysis to automotive and cloud application architecture, including compute, storage, cost and performance assessments. As AI becomes embedded in modern engineering workflows, we value engineers who can thoughtfully integrate AI into design, development, and decision-making.

Role Description Multimodal Analysis Framework (MAF) is an end‑to‑end platform designed to process diverse content sources—including video, images, audio, and documents—to generate rich, structured metadata. The platform unifies multiple ML/AI models to extract curated insights at scale, tailored to specific business needs. MAF supports both on‑demand workloads (batch uploads, ad‑hoc analysis) and real‑time streaming workflows, enabling continuous metadata generation for live content streams. Customers can define their metadata requirements—such as entity extraction, scene segmentation, object detection, transcription, summarization, or multimodal correlation—and the framework orchestrates the appropriate models and toolchains to deliver high‑quality outputs. Through flexible APIs and UI‑based workflows, customers and internal teams can visualize metadata, trigger enrichment, monitor processing, and integrate results into downstream applications. The platform emphasizes modularity, scalability, and extensibility to support new ML models, LLM‑based agents, and cross‑modal inference as use cases evolve. We are looking for a mid-level Backend Engineer to join our Machine Learning Platform team. This role focuses on building scalable backend systems that power ML workloads, including video, image, and document processing, and enable LLM-driven applications through agents and MCP servers. You will work primarily in Golang, deploy and operate services on Kubernetes, manage infrastructure with Terraform, and build on AWS. A core part of the role is designing platform capabilities that allow LLMs to safely and reliably interact with tools, data, and services via agent frameworks and MCP servers. Qualifications - 3–6 years of professional software engineering experience. - Strong backend engineering experience with Golang. - Experience building and operating APIs (REST and/or gRPC) in production. - Hands-on experience with Kubernetes in production environments. - Experience using Terraform for infrastructure provisioning and deployment. - Solid working knowledge of AWS cloud services and core architectural concepts. - Experience building or supporting ML processing pipelines (video, image, or document). - Practical experience using LLMs in production systems. - Experience developing agents and/or MCP servers, or equivalent tool-integration platforms. Requirements - Design, build, and maintain high-performance backend services in Golang for ML and AI platform use cases. - Develop REST and gRPC APIs for inference, processing pipelines, orchestration, and platform services. - Implement asynchronous and distributed processing patterns (workers, queues, event-driven systems). - Ensure backend services meet production standards for scalability, reliability, and security. - Build and operate backend systems supporting video processing (frame extraction, metadata generation, embeddings, indexing). - Build and operate backend systems supporting image processing (OCR, classification, detection, embedding generation). - Build and operate backend systems supporting document processing (parsing, layout analysis, chunking, OCR, retrieval pipelines). - Integrate ML inference services into backend workflows with attention to latency, throughput, and cost. - Work closely with ML engineers and data scientists to productionize models and pipelines. - Build LLM-enabled backend services using structured prompting, tool/function calling, and retrieval-augmented generation (RAG). - Design and implement agentic workflows (multi-step reasoning, tool orchestration, retries, guardrails). - Develop and operate MCP servers that expose internal platform capabilities (search, retrieval, processing, data access) to LLM-based applications. - Enforce security, access control, and observability for agent and MCP interactions. - Design and maintain vector-based retrieval systems using Milvus. - Implement embedding ingestion, indexing, and query pipelines at scale. - Optimize retrieval quality, latency, and relevance for downstream LLM applications. - Deploy and operate backend and ML services on Kubernetes (scaling, rollouts, resource management). - Use Terraform for infrastructure provisioning and continuous delivery of cloud resources. - Build and operate primarily on AWS, leveraging services such as compute, networking, and IAM; object storage; managed Kubernetes; logging and monitoring services. - Implement observability using logs, metrics, and traces; define SLOs and alerts. - Write automated tests (unit, integration) and contribute to CI/CD pipelines. - Participate in on-call rotations and incident response; drive post-incident improvements. Benefits - Base pay range: $142,651.46 - $213,977.19, dependent on job-related, non-discriminatory factors such as experience. - Most sales positions are eligible for a Commission under the terms of an applicable plan. - Most non-sales positions are eligible for a Bonus. - Best-in-class benefits to eligible employees, personalized to meet the needs of employees.