Software Engineer – Python (AI)

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:

• Experience with LangGraph, CrewAI, or Autogen for agent-based AI.
• Large-scale deployment of GenAI/ML projects with MLOps/LLMOps best practices.
• Experience handling scalable data pipelines (BigQuery, Synapse).
• Strong understanding of cloud computing architectures (Azure, AWS, GCP).

Behavioral Attributes:

• Strong ownership mindset, capable of leading end-to-end project deliverables.
• Ability to align AI solutions with business objectives and data requirements.
• Excellent communication and collaboration skills for cross-functional projects.