JOB DETAILS:
Role: Senior AI/ML Engineer (Generative AI & ML Ops)
Location: Bangalore, India (Hybrid)
Type of Hiring: Full Time
Job Description:
Required Skills and Expertise:
AI/ML Strategy and Leadership:
- Define the AI/ML strategy and roadmap aligned with the product vision.
- Identify and prioritize AI/ML use cases, including classical ML, Generative AI, and Agentic AI, relevant to our product offerings.
- Build and lead a high-performing AI/ML team by mentoring and upskilling existing non-AI/ML team members.
- Stay updated with the latest advancements in AI/ML, generative AI, Agentic AI and ML Ops, and apply them to solve business problems.
AI/ML Development:
- Proficient in Python programming and libraries like NumPy, Pandas, Scikit-learn, and Matplotlib.
- Strong understanding of machine learning algorithms, deep learning architectures, and generative AI models.
- Design, develop, and deploy classical machine learning models, including supervised, unsupervised, and reinforcement learningtechniques.
- Hands on Experience in AI/ML Framework like: Scikit-learn, XG Boost, TensorFlow, Py Torch, Keras, Hugging Face, OpenAI APIs (e.g., GPT models).
- Experience with feature engineering, model evaluation, and hyperparameter tuning.
- Experience with Lang Chain modules, including Chains, Memory, Tools, and Agents.
- Build and fine-tune generative AI models (e.g., GPT, DALL-E, Stable Diffusion) for specific use cases.
- Must Have Exp. in any Agentic AI framework. Leverage LLMs (e.g., GPT, Claude, LLaMA) and multi-modal models to build intelligent agents that can interact with users and systems.
- Design and develop autonomous AI agents capable of reasoning, planning, and executing tasks in dynamic environments.
- Implement prompt engineering and fine-tuning of LLMs to optimize agent behaviour for specific tasks.
- Optimize models for performance, scalability, and cost-efficiency.
ML Ops and DevOps for AI/ML:
- Establish and maintain an end-to-end MLOps pipeline for model development, deployment, monitoring, and retraining. Automate model training, testing, and deployment workflows using CI/CD pipelines.
- Implement robust version control for datasets, models, and code.
- Monitor model performance in production and implement feedback loops for continuous improvement.
- Proficiency in MLOps tools and platforms such as MLflow, Kubeflow, TFX, SageMaker.
- Familiarity with CI/CD tools like Jenkins, GitHub Actions, or GitLab CI for AI/ML workflows.
- Expertise in deploying models on cloud platforms (AWS, Azure, GCP)
