Key Responsibilities
- Model Development: Design, develop, and fine-tune Generative AI models (e.g., LLMs, GANs, VAEs) for applications such as text generation, image and video analysis and insight generation.
- End-to-End Project Ownership: Collaborate with stakeholders to define project requirements, develop AI pipelines, and deploy models into production environments.
- Application Integration: Work closely with application development teams to integrate AI models into web, mobile, or enterprise applications, ensuring seamless functionality.
- Data Processing: Preprocess and analyze large datasets to prepare them for model training, including data cleaning, feature engineering, and augmentation.
- Testing & Validation: Conduct rigorous testing, validation, and monitoring of AI models to ensure accuracy, robustness, and reliability in production.
- Collaboration: Partner with product managers, software engineers, and data scientists to align AI solutions with business objectives and technical requirements.
- Stay Updated: Keep abreast of the latest advancements in GenAI, machine learning, and related fields to propose innovative solutions.
Qualifications
- Education: Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field. Master’s degree is a plus.
- Experience:
- 1-4 years of experience in AI/ML development, with exposure to Generative AI, deep learning, NLP, Machine Learning, Computer Vision and Speech technology frameworks.
- Hands-on experience with end-to-end AI project lifecycles, from data preparation to model deployment.
- Experience collaborating with software development teams to integrate AI solutions into applications.
- Technical Skills:
- Proficiency in Python and relevant AI/ML libraries (e.g., TensorFlow, PyTorch, Hugging Face, scikit-learn, llama-index, langchain).
- Familiarity with Generative AI techniques (e.g., LLMs, GANs, diffusion models).
- Proficiency in writing prompts and Fine-tuning LLM
- Experience with cloud platforms (e.g., AWS, Azure, GCP) for model training and deployment along with working with Open-source model deployment
- Knowledge of software development practices, including version control (Git), CI/CD pipelines, and containerization (Docker).
- Understanding of REST APIs, microservices, or web frameworks (e.g., Flask, FastAPI) for application integration.
- Basic knowledge of data engineering tools (e.g., SQL, Pandas, Spark) for data preprocessing.
- Soft Skills:
- Strong problem-solving skills and a proactive approach to tackling challenges.
- Excellent communication skills to collaborate with technical and non-technical stakeholders.
- Ability to work in a fast-paced, team-oriented environment.
- Nice-to-Have:
- Experience with MLOps tools (e.g., MLflow, Kubeflow) for model tracking and deployment.
- Familiarity with front-end or back-end development (e.g., JavaScript, React, Node.js).
- Contributions to open-source AI projects or a portfolio showcasing relevant work.