Key Responsibilities:

Conduct original research on generative AI models, focusing on architectures, training methods, fine-tuning, and evaluation strategies.
Build Proof of Concepts (POCs) with emerging AI innovations and assess feasibility for production deployment.
Design and experiment with multimodal generative models (text, image, audio, and other modalities).
Develop autonomous, agent-based AI systems (agentic AI) capable of adaptive decision-making.
Lead the design, training, fine-tuning, and deployment of generative AI systems on large-scale datasets.
Optimize AI algorithms for efficiency, scalability, and computational performance using parallelization, distributed systems, and hardware acceleration.
Manage data preprocessing and feature engineering, including cleaning, normalization, dimensionality reduction, and feature selection.
Evaluate and validate models using industry-standard benchmarks, iterating to achieve target KPIs.
Provide technical leadership and mentorship to junior researchers and engineers.
Document research findings, model architectures, and experimental outcomes in technical reports and publications.
Stay updated with latest advancements in NLP, deep learning, and generative AI, fostering innovation within the team.

Mandatory Technical & Functional Skills:

Strong expertise in PyTorch or TensorFlow.
Proficiency in deep learning architectures: CNN, RNN, LSTM, Transformers, LLMs (BERT, GPT, etc.).
Experience fine-tuning open-source LLMs (Hugging Face, LLaMA 3, BLOOM, Mistral AI).
Hands-on knowledge of PEFT techniques (LoRA, QLoRA, etc.).
Familiarity with emerging AI frameworks & protocols (MCP, A2A, ACP).
Deployment experience with cloud AI platforms: GCP Vertex AI, Azure AI Foundry, AWS SageMaker.
Proven track record in building POCs for cutting-edge AI use cases.

Preferred Technical & Functional Skills:

Behavioral Attributes:

Software Engineer – Python (AI)