Senior AI/ML Engineer/ Developer

RADcube; Hyderabad; On-site; INR 2,500,000 - 3,500,000; Full time

Job summary

RADcube is looking for an AI/ML Lead in Hyderabad, India. You will architect ML systems and lead the engineering team to deliver innovative AI solutions for healthcare and enterprise applications. The role requires a strong background in AI/ML engineering, with hands-on experience in deployment, mentoring, and leading technical discussions. Candidates should have 5–7 years of experience, especially in NLP and cloud platforms.

Qualifications

• 5-7 years hands-on AI/ML engineering with 2+ years in a lead role.
• Production Python proficiency; experience with PyTorch, TensorFlow.
• Strong ML algorithm understanding: CNN, RNN, Transformers.

Responsibilities

• Architect and lead ML systems for RADlabs and Versante.
• Design and implement production ML pipelines.
• Build and mentor AI/ML engineering team.

Skills

AI/ML engineering

NLP

Production Python

Deployment of chatbots

Cloud ML platforms

Data engineering

Education

BS/MS in CS, AI/ML, Data Science

Tools

PyTorch

TensorFlow

Hugging Face Transformers

SQL

Docker

Kubernetes

Job description

Why This Role Exists

RADlabs is a research lab that publishes papers. It’s a production AI shop that ships systems into healthcare organizations, government agencies, and financial institutions where accuracy, compliance, and reliability aren’t optional. The AI/ML Lead is the technical spine. You’ll architect the ML pipelines behind intelligent document processing (already cutting review time by 70%), build the NLP engines powering Versante’s OSMeBuddy health conversations, design the syndemic intelligence models inside VersanteConnect, and stand up RADcube’s GenBI analytics platform. This is hands‑on keyboard plus team leadership plus client‑facing architecture—not a management role that’s lost touch with code.

What You Will Do

• Architect and lead ML systems for RADlabs (IDP, GenBI, AI Maturity tools) and Versante (OSMeBuddy NLP, VersanteConnect syndemic intelligence): end‑to‑end from data pipelines to production deployment
• Design and implement production ML pipelines: ingestion, feature engineering, training, evaluation, deployment, monitoring, retraining (MLOps)
• Build and mentor AI/ML engineering team (3–5 engineers): code reviews, architecture decision records, technical standards, and growth plans
• Develop and deploy NLP/LLM solutions: conversational AI, text classification, NER, sentiment analysis, and RAG architectures using both proprietary and open‑source models (GPT, Claude, Llama, HuggingFace)
• Build chatbot and conversational AI applications using Microsoft Bot Framework, Dialogflow, OpenAI APIs, or equivalent platforms—to integration into healthcare and enterprise workflows
• Design and deploy deep‑learning models (CNN, RNN, Seq2Seq, Transformers, LLMs) for document processing, form extraction, and compliance verification.
• Explore and implement advanced AI techniques: RLHF, prompt engineering optimization, few‑shot/zero‑shot learning, and multimodal AI.
• Embed responsible AI: bias detection, explainability (SHAP/LIME), fairness metrics, governance aligned with NIST AI RMF.
• Deploy on AWS/Azure using Docker/Kubernetes and serverless; establish CI/CD for ML with DevOps.
• Evaluate emerging tech (foundation models, fine‑tuning strategies, agentic AI frameworks like LangGraph/CrewAI) and make build‑vs‑buy recommendations for product roadmap.
• Write technical approach narratives and architecture diagrams for RFP responses; deliver client‑facing AI demos and capability presentations.
• Contribute thought leadership: blog posts, conference talks, Databricks/AI community engagement.

Requirements

What You Bring

• 5–7 years hands‑on AI/ML engineering, with 2+ years in lead/senior/architect role owning system‑level decisions.
• Production Python proficiency; deep experience with PyTorch or TensorFlow, Scikit‑learn, Hugging Face Transformers.
• Strong understanding of ML algorithms (classical and deep): KNN, SVM, Random Forest, CNN, RNN, Seq2Seq, Transformers, and LLMs.
• NLP/LLM production experience (Non‑Negotiable): fine‑tuning, prompt engineering, RAG architectures, conversational AI deployment—not just notebooks or Kaggle.
• Chatbot/Conversational AI experience: proven deployment of chatbots or AI assistants using Microsoft Bot Framework, Dialogflow, OpenAI APIs, or equivalent in production environments.
• Cloud ML platform experience: AWS SageMaker, Azure ML, or GCP Vertex AI with real deployment artifacts—not just certification projects.
• Data engineering foundation: SQL, Spark/Databricks, Airflow or equivalent orchestration, data pipeline design.
• Shipped AI systems in at least one of: healthcare, government, financial services, education. Domain context matters.
• Technical leadership: architecture ownership, team mentoring, sprint‑level technical planning, cross‑functional communication.
• Clear communicator who can explain model behavior, trade‑offs, and limitations to non‑technical executives, clients, and grant reviewers.
• BS/MS in CS, AI/ML, Data Science, Mathematics, or related quantitative field.

Bonus Points

• Experience with RLHF and advanced optimization strategies.
• Healthcare AI regulatory: HIPAA technical safeguards, FDA AI/ML guidance, FHIR/HL7 interoperability.
• Graph databases (Neo4j) and knowledge graphs for healthcare ontologies or syndemic modeling.
• Published research, conference talks, or open‑source contributions in AI/ML.
• AWS ML Specialty, Azure AI Engineer, or Google Professional ML Engineer certification.
• Databricks, Snowflake, or modern lakehouse experience.