AI Technical Lead

Market Securities Group

Architect & Build AI Systems: Design and implement end-to-end AI applications using LLMs, multi-agent workflows, and automation frameworks (e.g., LangChain, LangGraph), moving rapidly from PoC to production.

Hands-On Model Development: Fine-tune, chain, and deploy LLMs and other ML models actively writing and reviewing code.

Integrate AI into Core Products: Collaborate with engineering teams to embed AI capabilities into new and existing products, ensuring scalability, maintainability, and business impact.

Shape AI Processes & Governance: Establish standards for data quality, model validation, responsible AI practices, and deployment workflows in collaboration with compliance and governance teams.

Stay Ahead of the Curve: Monitor and explore emerging tools and trends in AI/ML, automation, and data engineering to maintain competitive advantage.

Measure & Iterate: Define KPIs and success metrics for AI initiatives, using data-driven approaches to refine models, workflows, and product direction.

Qualifications

• Experience: 5+ years in product or technical leadership with a proven track record of delivering complex, data-intensive technology products.
• Programming & Development: Proficiency in Python, Jscript, DB (SQL,NoSQL), C#
• AI/ML Frameworks: Hands-on experience with PyTorch, TensorFlow, Hugging Face, Scikit-learn, Transformers....
• Core ML Competencies:
  
  • Supervised Learning: Data classification, regression, and time-series forecasting.
  • Unsupervised Learning: Clustering, anomaly detection, and dimensionality reduction.
  • NLP: LLMs, embeddings, RAG, prompt engineering, text classification, and sentiment analysis.
  • Model Optimization: Fine-tuning, transfer learning, hyperparameter tuning, and model evaluation/validation.
  • Automation & Orchestration: N8n, AI agents, LangChain, LangGraph, workflow engines, and MCP.
• Technical Fluency: Strong understanding of software development practices, APIs, and cloud platforms (AWS, Azure, GCP). Experience with data architectures and building/maintaining data pipelines is highly valued.
• Portfolio Requirement: Demonstrated portfolio of AI/ML projects (e.g., GitHub repositories, technical case studies, production AI deployments, or published research) showcasing end-to-end solution development and integration.