Position: Assistant / Executive to CTO Location: Bangalore (Hybrid) Type: Full-Time Experience Required: 8+ Years We are looking for a seasoned Assistant/Executive to the CTO who will act as a strategic partner in driving technology, innovation, and business alignment. This role requires deep expertise in biotech, life sciences, and business strategy, along with the ability to translate scientific and technological initiatives into actionable business outcomes. Key Responsibilities Support the CTO in strategic planning, decision-making, and execution of technology and innovation roadmaps. Conduct market research and competitive analysis in biotech and life sciences to inform strategic initiatives. Drive alignment between technology and business strategies, ensuring measurable impact. Prepare executive-level presentations, reports, and communication for internal and external stakeholders. Oversee and monitor key projects, ensuring milestones are achieved. Act as a liaison between the CTO and cross-functional teams, fostering collaboration and accountability. Manage sensitive information with confidentiality and discretion. Mandatory Skills & Requirements 8+ years of experience in strategy, business planning, or executive support roles. Strong background in biotech, life sciences, or healthcare technology. Proven ability to lead or influence strategic initiatives at senior levels. Excellent communication, analytical, and presentation skills. Strong organizational and multitasking abilities in a dynamic environment. Educational Qualifications Master’s degree in Life Sciences, Biotechnology, Business Administration, or related field (preferred). MBA with biotech/life sciences specialization is highly desirable. Preferred Qualifications Experience in biotech/healthcare startups or global life science organizations. Exposure to R&D strategy, innovation management, or corporate development. Job Type: Full-time Pay: Up to ₹800,000.00 per year Experience: strategy, business planning, or executive support: 8 years (Required) biotech, life sciences, or healthcare technology background: 8 years (Required) strategic planning, decision-making, and execution: 10 years (Required) Work Location: In person
Experience: 6 – 10 years Location: Hyderabad - Work from Office Department: Enterprise Data & AI Reports To: Director – AI/ML Solutions Core Technical Expertise Programming & Scripting: Strong Python and Bash scripting expertise. ML Algorithms & Frameworks: Proficiency in ML algorithms (XGBoost, LightGBM, CatBoost) and frameworks (TensorFlow, PyTorch, Keras). ML Deployment & Operations (MLOps): Proven experience in designing and deploying ML models at scale. Experience with feature stores, model serving, and ML orchestration tools. Expertise in data pipeline orchestration (Airflow, Prefect, or Kubeflow). Experience with feature engineering, model versioning, and A/B testing. Strong understanding of machine learning lifecycle management and observability. Infrastructure & DevOps: Familiarity with containerization (Docker, Kubernetes) and cloud platforms (AWS, Azure, or GCP). Hands-on with Git, CI/CD, REST APIs, and API gateways. Data & Databases: Knowledge of SQL/NoSQL databases, data lakes, and vector databases. Professional & Leadership Skills Proven ability to lead and mentor ML engineering teams. Excellent troubleshooting, performance tuning, and incident resolution skills. Excellent communication, analytical thinking, and collaboration skills. Nice-to-Have Skills Experience with LLM fine-tuning, RAG (Retrieval-Augmented Generation), or Generative AI pipelines. Familiarity with MCP (Model Context Protocol) and agent orchestration frameworks (LangChain, LangGraph, or Haystack). Exposure to data governance frameworks and ML observability tools (e.g., Arize, EvidentlyAI). Understanding of business use cases in FinTech, Healthcare, or Supply Chain analytics. Job Type: Full-time Pay: ₹272,589.86 - ₹1,523,983.80 per year Application Question(s): How many years of professional experience do you have specifically in Machine Learning Engineering (MLE) or MLOps? Have you successfully designed and deployed ML models at scale in a production environment? (Yes/No) Which programming languages are you proficient in for ML development and infrastructure scripting? Which tool(s) have you used to orchestrate ML/Data pipelines in a professional setting? Which platforms and tools are you experienced with for building and running production ML systems? Work Location: In person