Data Scientist, Advanced

Remote Work: Hybrid

Highly skilled and motivated Data Scientist (LLM Specialist) to join our AI/ML team. This role is ideal for an individual passionate about Large Language Models (LLMs), workflow automation, and customer-centric AI solutions. You will be responsible for building robust ML pipelines, designing scalable workflows, interfacing with customers, and independently driving research and innovation in the evolving agentic AI space.

Key Responsibilities:

· LLM Development & Optimization: Train, fine-tune, evaluate, and deploy Large Language Models (LLMs) for various customer-facing applications.

· Pipeline & Workflow Development: Build scalable machine learning workflows and pipelines that facilitate efficient data ingestion, model training, and deployment.

· Model Evaluation & Performance Tuning: Implement best-in-class evaluation metrics to assess model performance, optimize for efficiency, and mitigate biases in LLM applications.

· Customer Engagement: Collaborate closely with customers to understand their needs, design AI-driven solutions, and iterate on models to enhance user experiences.

· Research & Innovation: Stay updated on the latest developments in LLMs, agentic AI, reinforcement learning with human feedback (RLHF), and generative AI applications. Recommend novel approaches to improve AI-based solutions.

· Infrastructure & Deployment: Work with MLOps tools to streamline deployment and serve models efficiently using cloud-based or on-premise architectures, including Google Vertex AI for model training, deployment, and inference.

· Foundational Model Training: Experience working with open-weight foundational models, leveraging pre-trained architectures, fine-tuning on domain-specific datasets, and optimizing models for performance and cost-efficiency.

· Cross-Functional Collaboration: Partner with engineering, product, and design teams to integrate LLM-based solutions into customer products seamlessly.

· Ethical AI Practices: Ensure responsible AI development by addressing concerns related to bias, safety, security, and interpretability in LLMs.

· Experience: experience in ML, NLP, or AI-related roles, with a focus on LLMs and generative AI.

· Programming Skills: Proficiency in Python and experience with ML frameworks like TensorFlow, PyTorch

· LLM Expertise: Hands-on experience in training, fine-tuning, and deploying LLMs

(e.g., OpenAI’s GPT, Meta’s LLaMA, Mistral, or other transformer-based architectures).

· Foundational Model Knowledge: Strong understanding of open-weight LLM architectures, including training methodologies, fine-tuning techniques, hyperparameter optimization, and model distillation.

· Data Pipeline Development: Strong understanding of data engineering concepts, feature engineering, and workflow automation using Airflow or Kubeflow.

· Cloud & MLOps: Experience deploying ML models in cloud environments like AWS, GCP (Google Vertex AI), or Azure using Docker and Kubernetes.

· Model Serving & Optimization: Proficiency in model quantization, pruning, distillation, and knowledge distillation to improve deployment efficiency and scalability.

· Research & Problem-Solving: Ability to conduct independent research, explore novel solutions, and implement state-of-the-art ML techniques.

· Strong Communication Skills: Ability to translate technical concepts into actionable insights for non-technical stakeholders.

· Version Control & Collaboration: Proficiency in Git, CI/CD pipelines, and working in cross-functional teams.

Nice-to-Have:

· Experience with Reinforcement Learning (RLHF) for LLMs.

· Knowledge of vector databases and retrieval-augmented generation (RAG) architectures.

· Familiarity with multi-modal AI models (vision-language models, speech-to-text, etc.).

· Understanding of agentic AI frameworks (e.g., AutoGPT, LangChain, LlamaIndex).

· Hands-on experience with Google Vertex AI Pipelines, AutoML, and model monitoring.

We are seeking LLM enthusiast with a knack for research, customer interaction, and building impactful AI solutions

• Education: Bachelor's/Master’s/Ph.D. in Computer Science, Machine Learning, AI, Data Science, or a related field.
• B.Tech/M.Tech/PhD in CS/ML/Statistics
• Preferably 8+ years’ experience as Data Scientist OR in
  related field (Statistics / Operation Research) OR as System
  Architect / Enterprise Architect
• Design and conduct Analysis/Experiments (Plan Analysis and
  address competing explanations, Determine best way to evaluate
  results, Explore structured and unstructured data appropriately,
  Apply appropriate algorithms, Clearly document and articulate
  findings),
  Incorporate Analysis into pipelines and pipelines into
  business/Engineering stack (Read/write data to/from any
  format / location, incorporate complex matching and filtering,
  Make work compatible with/suitable for engineering stack,
  Discover needs of business, Navigate Business organization
  structure, package technical work for diverse audiences),
  Build the profession (Mentor/Coach Junior Data scientists,
  enable/upskill citizen data scientists)
• Problem Solving / Critical Thinking, Good business intuition,
  Programming and Coding (Python / R), Mathematics and
  Statistics (Equivalent to Graduate level Stat 101 and Math
  101), Machine Learning / Deep Learning / AI, Communication
  / Appropriate articulation, Data Architecture, Risk Analysis /
  Systems Engineering
•