Lead AI Engineer

4 weeks ago

Bangalore Urban, India Deep Tech Stars Full time

Salient features:

1.      Vision - To create an AI Finance Analyst that rethinks entire CFO stack from cashflow to planning

2.      Work on the latest tech - combination of traditional ML and GenAI, including Agents

3.      Tech Leadership role - You will be the first AI hire for the team and lead the direction of AI development

4.      Visibility - Work directly with veteran founders from ISB and IIM

5.      Well funded - Raised a large seed round from some of the top VCs in India

 

Role Overview:

As the Lead AI Engineer, you will be responsible for designing, training, and deploying AI models, with a specific focus on LLMs and agent-based systems. You’ll work across the entire AI pipeline, from identifying use cases to deploying and monitoring models in production. This role is ideal for a technically skilled individual contributor who can independently drive innovation and deliver impactful solutions.


Key Responsibilities:

  • LLM & NLP Model Development: Develop and fine-tune large language models (GPT-3/4, T5, BERT, etc.) for tasks like text generation, summarization, sentiment analysis, and information retrieval.
  • Agentic System Design: Build and deploy autonomous, goal-oriented agentic systems capable of interacting with complex environments to achieve specific tasks.
  • Project Ownership: Manage AI initiatives from ideation through to production deployment, independently handling all aspects of model lifecycle management.
  • Cross-Functional Collaboration: Partner with teams across product, engineering, and operations to translate business requirements into actionable AI projects, particularly in areas that leverage LLMs and agentic AI.
  • Model Optimization & Evaluation: Optimize models for scalability, accuracy, and efficiency; perform regular testing, monitoring, and tuning to maintain performance in production.
  • Research & Experimentation: Stay abreast of the latest AI advancements, including prompt engineering, reinforcement learning, and advancements in agent-based architectures, and evaluate their applicability.
  • Documentation & Knowledge Sharing: Document architectures, model performance, and lessons learned, while actively sharing insights and best practices across the organization.


Requirements:

  • Experience: 6+ years in AI and machine learning, with hands-on expertise in LLMs and agentic system development and deployment.
  • Proficiency in NLP & LLMs: Strong experience with large language models (GPT-3/4, Claude etc.) and understanding of transformer architectures, pretraining/fine-tuning, prompt engineering, and task adaptation.
  • Agentic Systems Expertise: Knowledge in creating and deploying agentic systems with a goal-oriented design, leveraging reinforcement learning (RL) or other methods for autonomous task management.
  • Technical Skills: Advanced proficiency in Python, ML libraries (Hugging Face Transformers, TensorFlow, PyTorch), and data processing libraries (Pandas, NumPy).
  • MLOps & Deployment: Experience with MLOps practices, particularly for deploying large models and agentic systems in production environments (Docker, Kubernetes, MLFlow, or similar).
  • Data Management: Familiarity with data engineering principles, including handling large datasets and real-time processing for model training, and experience with cloud platforms (AWS, GCP, Azure).
  • Independent Problem Solving: Strong analytical skills with a proven track record of working independently to solve complex AI challenges.
  • Communication Skills: Ability to clearly articulate technical concepts and AI-driven insights to non-technical stakeholders and document work for continuity.


Nice-to-Have:

  • Advanced Degree: Master’s or Ph.D. in Artificial Intelligence, Machine Learning, Computer Science, or a related field.
  • Knowledge of Financial AI Applications: Experience applying AI in the finance sector, understanding compliance, privacy, and domain-specific challenges.
  • Familiarity with RL Techniques: Proficiency in reinforcement learning, especially with agentic system implementations.

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