AI Engineer
About the Role
We are seeking an experienced AI Engineer to design and optimize AI/ML systems, leveraging cutting-edge artificial intelligence and machine learning techniques. In this role, you will work with large language models (LLMs), fine-tune AI systems, and develop scalable Agentic AI solutions for financial and enterprise applications.
This is an exciting opportunity to be at the forefront of AI innovation, working with the latest advancements in natural language processing, reinforcement learning, and scalable AI infrastructure.
What Awaits You:
Latest Tools & Tech – Work with cutting-edge technologies to stay ahead.
Career Growth – Access training programs for upskilling or reskilling to build your portfolio.
Great Pay & Perks – Competitive salary and bonuses to reward your expertise and contributions.
What You'll Do:
Design and develop scalable AI/ML models and pipelines for a variety of applications.
Fine-tune large language models using supervised learning and alignment techniques such as DPO (Direct Preference Optimization), ORPO (Odds Ratio Preference Optimization) and RLHF (Reinforcement Learning from Human Feedback).
Train and evaluate embedding models to improve downstream task performance.
Integrate structured and unstructured data sources to enhance AI-driven decision-making.
Improve model accuracy, contextual coherence, and response quality in generative AI outputs.
Develop and optimize training, evaluation, and deployment pipelines for production environments.
Leverage and fine-tune open-source AI models for specific use cases.
Collaborate with cross-functional teams to deploy models at scale in real-world systems.
What We Need From You:
Core AI & ML Skills
Experience with LLM fine-tuning and training (e.g., Hugging Face, Claude API).
Strong understanding of Transformer models and NLP architectures.
Expertise in reinforcement learning and self-supervised learning.
Hands-on experience with RLHF (Reinforcement Learning from Human Feedback).
Programming & Frameworks
Proficiency in Python, and experience with PyTorch, or JAX.
Familiarity with LLM in stateful agentic workflows and multi-agent orchestration.
Familiarity with Agent SDKs (LangGraph), MCP and A2A protocols.
Strong skills in working with APIs for AI system integration (e.g., OpenAI, Anthropic, Hugging Face).
Experience with SQL, NoSQL and Vector-based databases for data handling.
Familiarity with distributed computing and scalable ML model training.
Data Engineering & Deployment
Solid understanding of ETL pipelines for processing large-scale datasets.
Experience deploying AI models in cloud environments (AWS, Azure, GCP).
Knowledge of containerization (Docker, Kubernetes) and MLOps practices.