Full-Stack AI Engineer / Data Scientist

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: