Staff Engineer, LLM,AI

REQUIREMENTS:

• Total Experience: 6+ years in Data Science / Applied Machine Learning, including 1–2+ years in Generative AI / LLM-based solutions.
• Strong proficiency in Python, with hands-on experience using LangChain, Pandas, NumPy, scikit-learn, Hugging Face Transformers, Pydantic, etc.
• Experience deploying GenAI workloads on AWS, including Amazon Bedrock, SageMaker, Lambda, ECS, API Gateway, Step Functions, CloudWatch.
• Deep understanding of LLM architectures, tokenization, RAG, embeddings, and vector databases such as FAISS, Qdrant, Pinecone, OpenSearch.
• Hands-on experience implementing agent-based architectures and multi-step reasoning pipelines.
• Strong expertise in data wrangling, feature engineering, and building ML models for classification, regression, clustering, and NLP.
• Experience with CI/CD, API development, and integrating GenAI models into production-grade applications.
• Familiarity with LangGraph, AutoGPT, CrewAI, or other custom agent frameworks.
• Knowledge of vector databases such as Qdrant, Snowflake Cortex, embedding models, and custom RAG pipelines.
• Prior contributions to open-source GenAI projects or research work in LLM/NLP domains.
• Hands-on experience with MLOps, including model versioning and monitoring tools (MLflow, Weights & Biases, SageMaker Model Monitor).
• Exposure to fine-tuning and parameter-efficient tuning (LoRA, QLoRA) of LLMs.
• Understanding of data privacy, PII redaction, security, and compliance considerations in GenAI applications.
• Strong analytical thinking, problem-solving, and communication skills.

RESPONSIBILITIES:

• Build, deploy, and optimize Generative AI and LLM-based applications for scalable enterprise use cases.
• Design and implement RAG systems, vector search pipelines, and agent-based reasoning workflows.
• Architect and develop end-to-end ML/GenAI solutions including data ingestion, preprocessing, experimentation, evaluation, and deployment.
• Work with cross-functional teams to define problem statements and translate business requirements into technical solutions.
• Develop and maintain GenAI-powered APIs, microservices, and automation workflows using AWS services.
• Optimize model performance, cost efficiency, token usage, prompt structures, and overall system reliability.
• Build reusable GenAI components including prompt templates, toolchains, memory modules, evaluators, and monitoring dashboards.
• Implement robust MLOps practices for version control, experiment tracking, CI/CD pipelines, and automated deployment.
• Ensure high-quality production readiness through testing, model validation, drift detection, and continuous monitoring.
• Conduct research, POCs, and benchmarking of emerging LLMs, embeddings, vector databases, and agent frameworks.
• Prepare documentation, architecture diagrams, and best practices for internal and client-facing teams.
• Collaborate with engineering, product, and cloud teams to ensure seamless integration of GenAI features into applications.
• Mentor junior engineers and contribute to knowledge sharing, code reviews, and innovation initiatives.
• Ensure compliance with security, privacy, and responsible AI guidelines across all GenAI implementations.

Bachelor’s or master’s degree in computer science, Information Technology, or a related field.