Role & responsibilities

ML/AI Architecture

- Design and architect end-to-end ML and GenAI solutions using Databricks, Python and cloud technologies.

- Define scalable ML architectures covering data ingestion, feature engineering, model development, deployment and monitoring.

- Develop solutions using traditional ML, NLP, deep learning and LLM-based architectures.

- Evaluate and recommend appropriate ML/AI frameworks, platforms and cloud services.

Databricks & MLOps

- Build and productionize ML workloads on Databricks.

- Leverage MLflow for experiment tracking, model management, deployment and lifecycle governance.

- Work with Apache Spark/PySpark for large-scale data processing.

- Establish MLOps practices including CI/CD, model versioning, monitoring and governance.

- Implement model and data drift monitoring and define remediation strategies.

GenAI / LLM Solutions

- Architect and implement LLM-powered applications, including RAG and enterprise AI solutions.

- Work with Hugging Face, LangChain and OpenAI technologies.

- Design embedding and vector-search architectures using vector databases.

- Perform or guide LLM fine-tuning and model optimization.

- Evaluate LLM performance, accuracy, latency, cost and scalability.

Cloud & Production Deployment

- Deploy ML/AI solutions across AWS, Azure or GCP.

- Ensure solutions are scalable, secure, reliable and production-ready.

- Integrate ML platforms with enterprise data and application ecosystems.

- Troubleshoot performance and deployment issues in production environments.

Customer / Pre-Sales / Post-Sales

- Engage directly with customers to understand business and technical requirements.

- Lead technical discovery sessions, solution workshops and architecture discussions.





- Translate business problems into practical ML/AI and Databricks solutions.

- Prepare solution architectures, technical proposals, POCs and demonstrations.

- Support pre-sales activities, RFP/RFI responses and technical presentations.

- Provide post-sales architectural guidance and help customers successfully implement solutions.

- Communicate complex technical concepts effectively to mixed technical and business audiences.

Preferred candidate profile

- 9+ years of overall experience in Machine Learning, Data Science, AI/ML Engineering or related technologies.

- 6+ years of hands-on experience in ML/AI solution development and architecture.

- 3+ years of strong Databricks experience, preferably involving production ML workloads.

- Robust hands-on expertise in Python and SQL.

- Proven experience with Pandas, scikit-learn, MLflow, Gensim and NLTK.

- Strong experience with TensorFlow and/or PyTorch.

- Demonstrated experience taking ML models from POC to production.

- Experience deploying ML solutions on at least one major cloud: AWS, Azure or GCP.

- Practical experience with MLOps, model lifecycle management and drift monitoring.

- Strong understanding of LLMs, GenAI, Hugging Face, LangChain, OpenAI and vector databases.

- Experience with LLM fine-tuning, RAG, embeddings and prompt engineering is highly desirable.

- Apache Spark/PySpark and Databricks platform expertise preferred.

- 4+ years of pre-sales/post-sales or customer-facing technical experience is strongly preferred.

- Excellent stakeholder management and technical communication skills.

- Ability to act as a trusted technical advisor to enterprise customers.

- Comfortable leading architecture discussions, technical workshops, POCs and customer presentations.

📌 ML Architect Databricks (Indore)
🏢 Ktek Talent Solutions
📍 Indore