Full-Stack AI Engineer / Data Scientist – Agentic Systems

Core Tech Stack & Scope This role involves working deeply with multimodal foundation models, advanced agentic workflows (including Agent-to-Agent/A2A communication), Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG) pipelines, other emerging AI technologies, and MLOps subsystems. You will utilize cloud services (AWS and multicloud) alongside vector, graph, and traditional databases to develop scalable and robust AI solutions. Key Responsibilities Agentic & GenAI Application Development: Design and build advanced AI agentic systems, state machines, search-based conversational systems that solve complex business problems. Develop workflows leveraging Large Foundational Multimodal Models to process and reason across text, audio, and video modalities. Implement Model Context Protocol (MCP) servers/clients to standardize context exchange between agents, data sources, and external tools. Collaborate with AI Architects, Product Owners, and fellow developers to integrate AI capabilities into scalable, fair, and ethical end-user applications focusing on relevance and real-time performance. Full-Stack Engineering & Agentic SDLC: Leverage AI coding agents (e.g., Claude Code) daily to accelerate full-stack development cycles, maintaining high productivity across frontend, backend, and infrastructure tasks. Take end-to-end accountability for features: write high-quality, production-ready Python (and occasionally TypeScript) code with comprehensive testing and documentation. Manage the DevOps/MLOps lifecycle: containerize applications using Docker, configure CI/CD pipelines, and architect high-throughput, reliable cloud-native solutions on AWS/multicloud. Data Science, EDA & Strategy: Perform thorough Exploratory Data Analysis (EDA) to understand dataset characteristics, uncover patterns, detect biases, and identify data quality issues. Use statistical and visualization techniques to inform feature engineering, model selection, and optimization of foundation model-based applications. Design robust data pipelines to curate, preprocess, and structure diverse datasets that maximize LLM effectiveness and reduce bias. Algorithm Development & Optimization: Design, customize, optimize, and fine-tune LLM-based and traditional AI algorithms for specific use cases (e.g., text generation, summarization, AI agents, sequence modeling). Lead advanced prompt engineering strategies, utilizing zero-shot, few-shot and other paradigms to optimize model outputs without extensive fine-tuning. Implement pre-generative AI models (e.g., classification, clustering, regression) when they provide a more efficient, interpretable, or cost-efficient solution compared to LLMs. Optimize model inference speed, reduce latency (cold start reduction, caching strategies), and manage resource usage across cloud architectures. Evaluation, Observability & Continuous Improvement: Conduct rigorous experimentation (A/B testing) and implement automatic metric pipelines (e.g. BLEU/ROUGE, RAG retrieval accuracy, human rating frameworks, etc.) to evaluate generative and multimodal systems. Implement real-time algorithms monitoring and observability practices, ensuring visibility into pipelines behavior, drift detection, and anomaly identification using telemetry tools. Translate complex technical results into clear, actionable insights for stakeholders, driving data-driven decision-making. Practical Skills Required Experience: 7+ years of experience in AI/ML engineering and Data Science, with exposure to generative AI, agents and classical ML. 3+ years of hands-on experience with generative large language models and agents. Proven experience managing the end-to-end MLOps lifecycle and deploying models at scale. Proven experience leveraging AI coding agents within an agentic SDLC to accelerate feature delivery. Skills:

Practical Skills Required Experience: 7+ years of experience in AI/ML engineering and Data Science, with exposure to generative AI, agents and classical ML. 3+ years of hands-on experience with generative large language models and agents. Proven experience managing the end-to-end MLOps lifecycle and deploying models at scale. Proven experience leveraging AI coding agents within an agentic SDLC to accelerate feature delivery. Skills: Must-have: Expert Python: Advanced production-grade proficiency in Python. Cloud Platforms: Practical experience designing cloud solutions on AWS (familiarity with multicloud is a plus), utilizing GenAI-specific services (e.g., Amazon Bedrock, SageMaker). AI/GenAI Frameworks: Deep expertise with LangChain, LangGraph or similar LLM orchestration frameworks, agentic system design, and state machine logic. Building production-level AI systems including RAGs, Vector DBs, MCP, end-users, integrations, etc., but also observability, DevOps, durable and available system designs. Software Engineering Best Practices: Expertise in API design, CI/CD, Docker, Infrastructure as Code, and rigorous automated testing. aSDLC expertise: Proven experience in leveraging agents for SDLC (software development lifecycle). Databases: Snowflake, SQL, NoSQL, and data lakes. Data Skills: Strong background in statistics, data manipulation, synthetic data generation, and feature engineering. Could-have: Familiarity with TypeScript is considered an advantage - to have a common language with the full-stack Software Engineers. Regulatory Compliance: Proven experience in working within highly regulated industries. Capabilities: Problem-Solving Skills: Excellent analytical skills to tackle complex engineering and statistical challenges. Ownership & Leadership: Deep sense of accountability, eager to define architectural patterns, and able to step into a Tech Lead role when necessary. Consulting: Abilities to work closely with stakeholders across the enterprise to consult on the technological approaches to their business problems. Ethics: Strong understanding of biases, fairness, hallucination mitigation, and responsible AI deployment.

Must-have: Expert Python: Advanced production-grade proficiency in Python. Cloud Platforms: Practical experience designing cloud solutions on AWS (familiarity with multicloud is a plus), utilizing GenAI-specific services (e.g., Amazon Bedrock, SageMaker). AI/GenAI Frameworks: Deep expertise with LangChain, LangGraph or similar LLM orchestration frameworks, agentic system design, and state machine logic. Building production-level AI systems including RAGs, Vector DBs, MCP, end-users, integrations, etc., but also observability, DevOps, durable and available system designs. Software Engineering Best Practices: Expertise in API design, CI/CD, Docker, Infrastructure as Code, and rigorous automated testing. aSDLC expertise: Proven experience in leveraging agents for SDLC (software development lifecycle). Databases: Snowflake, SQL, NoSQL, and data lakes. Data Skills: Strong background in statistics, data manipulation, synthetic data generation, and feature engineering. Could-have: Familiarity with TypeScript is considered an advantage - to have a common language with the full-stack Software Engineers. Regulatory Compliance: Proven experience in working within highly regulated industries. Capabilities: Problem-Solving Skills: Excellent analytical skills to tackle complex engineering and statistical challenges. Ownership & Leadership: Deep sense of accountability, eager to define architectural patterns, and able to step into a Tech Lead role when necessary. Consulting: Abilities to work closely with stakeholders across the enterprise to consult on the technological approaches to their business problems. Ethics: Strong understanding of biases, fairness, hallucination mitigation, and responsible AI deployment. Qualifications The successful candidate should: Hold a B.Sc., B.Eng., M.Sc., M.Eng., Ph.D., or equivalent in Computer Science, Physics, Statistics, Mathematics, or a related field. Be deeply passionate about AI, staying continuously up-to-date with the latest developments in foundational models, agentic approaches, and classical ML. Be team-oriented, proactive, and collaborative, thriving in a fast-paced environment where roles are fluid and multidisciplinary. Be a great communicator, able to present complex findings clearly to both technical and non-technical audiences. Be able to communicate in English at the level of: C1+. Located in Hyderabad, India, with working hours structured to capture the 'golden hours' of overlap with Central European Time (typically running through the IST evening).

Core Tech Stack & Scope This role involves working deeply with multimodal foundation models, advanced agentic workflows (including Agent-to-Agent/A2A communication), Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG) pipelines, other emerging AI technologies, and MLOps subsystems. You will utilize cloud services (AWS and multicloud) alongside vector, graph, and traditional databases to develop scalable and robust AI solutions.