Role: Senior Engineer - Agentic AI
Location options: Any US Location

Job Description:

Preface: The Agentic AI Engineer is a hands-on development role at TCS (Americas) specializing in building and deploying AI agent solutions for clients. As businesses shift toward agentic AIautonomous systems that execute tasks independentlyroles like AI Agent Engineer have emerged. In this client-facing consulting position, youll work in a hybrid environment, delivering cutting-edge AI agents that blend large language models, custom prompts, data sources, and business logic. Projects can range from financial chatbots to manufacturing optimizers, requiring advanced prompt engineering, Retrieval-Augmented Generation (RAG), and strong software skills.

What You Would Be Doing
• Develop AI Agents & Applications: Code the core logic for AI agents, whether standalone or in multi-agent systems, enabling them to answer questions, generate content, or execute transactions.
• Coding with LLMs and Tools: Use Python or similar languages to integrate large language models (LLMs) and external tools (e.g., APIs, web search, databases).
• Prompt Engineering & Optimization: Craft, refine, and test prompts to guide agent behavior, including fallback strategies for uncertainty.
• Implement RAG for Knowledge: Connect AI agents to vector databases or search indices to ground outputs in up-to-date, domain-specific information.
• System Integration & APIs: Integrate AI agents with external systems (e.g., travel booking APIs, payment gateways), handling formatting, RESTful calls, and data responses as needed.
• Testing and Iteration: Simulate agent behavior, identify and fix failure modes, and tune prompts and code for high-quality results.
• Deploy AI Solutions: Package and deploy agent applications (Docker, cloud), ensuring scalability and proper configuration.
• Collaboration & Agile Delivery: Work with AI Architects, Data Engineers, and UX Developers in agile teams, contributing to sprints and client demos.
• Industry-Specific Customization: Tailor solutions for each industry, adapting compliance, personalization, and integration as needed.
• Adhere to AI Ethics & Safety: Implement guardrails, content moderation, and privacy measures, following TCSs responsible AI guidelines.

What Skills Are Expected
• Programming & Software Engineering: Expertise in Python (and optionally Java, JavaScript, or C#), unit testing, and version control (Git).
• AI/ML Knowledge: Solid grasp of machine learning and AI concepts, model behavior, and experience with NLP or chatbots.
• Prompt Engineering: Experience crafting and iterating prompts, including few-shot examples and output formatting techniques.
• RAG and Data Handling: Familiarity with embedding models, vector databases, and un structured data processing.
• API and Integration Skills: Building and consuming RESTful APIs, microservices, and handling JSON/XML data formats.
• Data Structures & Algorithms: Knowledge of lists, dictionaries, trees/graphs, and their application in efficient agent design.
• Debugging & Problem-Solving: Strong troubleshooting abilities to distinguish between model and code issues.
• Agile and Collaborative Mindset: Comfortable working in sprints, collaborating across teams, and communicating technical needs.
• Domain Adaptability: Ability to quickly learn new industry concepts for tailored agent solutions.
• Attention to Detail & Quality: Consideration of edge cases, proper data handling, and thorough testing.
• Ethical Awareness: Recognize bias, confidentiality issues, and flag questionable requests.

Key Technology Capabilities
• Languages & Frameworks: Mastery of Python for AI/ML, with exposure to JavaScript/TypeScript, FastAPI, or Flask for APIs.
• AI/ML Tools: Experience with AI model APIs (OpenAI, Azure OpenAI), and ML frameworks like PyTorch or TensorFlow.
• Agent Development Libraries: Hands-on with LangChain or similar frameworks for prompt management and agent logic.
• Databases & Data Access: Working with SQL, NoSQL, and vector databases (e.g., Pinecone, Weaviate) for data retrieval.
• DevOps & Deployment: Familiarity with Docker, CI/CD, and cloud deployment (AWS, Azure, GCP, Lambda/Functions).
• Version Control & Collaboration: Proficient with Git and DevOps platforms (GitHub, GitLab, Bitbucket).
• Testing Tools: PyTest, Postman, and AI evaluation methods.
• Cloud & Services: Practical knowledge of cloud AI offerings and environment configuration.
• Messaging & Async Processing: Experience with event-driven workflows (RabbitMQ, Kafka, SQS) is a plus.
• Monitoring & Logging: Implementing logging (Python logging, CloudWatch, Application Insights) for tracking and debugging.
• Frameworks for UI (optional): Familiarity with Streamlit or basic web development for internal agent demos.
• Security & Compliance Tools: Handling OAuth, encryption, and compliance libraries for regulated industries.
• Source Data Tools: Using NLP libraries for text preprocessing and embeddings (e.g., sentence-transformers, Jupyter notebooks).

Salary Range: $115,000 - $155,000 a year

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