Roles & Responsibilities:
- Agentic AI Development: Design, develop, and optimize domain adaptive agentic AI systems that helps in automating business processes.
- LLM Fine-Tuning: Work with large-scale pre-trained models (like Llama, Mistral etc.) to fine-tune with techniques like PEFT, SFT and adapt them for specific applications and domains. Evaluate and Optimize for performance, accuracy, and efficiency.
- Prompt Engineering: Design prompts with techniques like Chain of Thought, Few Shot to enhance model responses, ensuring that model outputs are aligned with use case requirements.
- AI Workflow Automation: Build end-to-end workflows for AI solutions, from data collection and preprocessing to training, deployment, and continuous improvement in production environments.
- Collaboration with Cross-functional Teams: Work closely with data scientists, software engineers, and product managers to define AI product requirements and deliver innovative solutions.
- Research & Development: Stay current with the latest research and developments in generative AI, deep learning, NLP, reinforcement learning, and related fields to ensure that the organization stays at the forefront of technology.
- Scaling and Deployment: Deploy machine learning models at scale, optimizing for latency, throughput, and robustness in production environments.
- Documentation & Reporting: Maintain clear documentation of models, workflows, and experiments, and communicate results effectively to stakeholders.
Required Skills & Qualifications:
- Experience:
- 1 to 3 years of hands-on experience in machine learning and AI engineering.
- Proven track record in working with LLMs such as Llama, Mistral and models like GPT, BERT, T5, or similar.
- Expertise in designing, fine-tuning, and deploying generative AI models and building agentic workflows.
- Strong experience in prompt engineering to optimize AI models performance.
- Technical Skills:
- Proficiency in Python, TensorFlow, PyTorch, or other ML frameworks.
- Proficiency in building agentic workflows with tools like Langgraph, CrewAI, Autogen, PhiData or similar.
- Familiarity with cloud platforms (AWS, GCP, Azure) for deployment and scaling of models.
- Experience with NLP tasks, such as text classification, text generation, summarization, and question answering.
- Knowledge of reinforcement learning, multi-agent systems, or other autonomous decision-making frameworks.
- Familiarity with SDLC life cycle , data processing tools (e.g., Pandas, NumPy, etc.) and version control (e.g., Git).
- Soft Skills:
- Strong problem-solving and analytical skills.
- Excellent communication and teamwork abilities to collaborate with stakeholders.
- Ability to work independently and drive projects to completion with minimal supervision.
Preferred Qualifications:
- Experience in deploying AI models at scale in production environments.
- Expertise in large-scale data processing, optimization techniques, and model deployment.