• Full-Stack AI Engineering: Hands-on development, deployment, and continuous improvement of sophisticated AI-driven features, leveraging Agile practices and top-tier coding standards.
• Agentic & Generative AI Technologies: Design and deploy intelligent, autonomous AI agents (LLMs, multi-agent systems) capable of planning, reasoning, and decision-making—solving complex HR and talent management challenges with next-gen AI.
• Orchestration & Tooling: Build frameworks for multi-agent orchestration, message passing, prompt engineering, vector databases (FAISS, Pinecone), and scalable knowledge graphs to enable robust agent collaboration and negotiation.
• Seamless Systems Integration: Fuse agentic and generative AI systems with modern APIs, REST/gRPC, user interfaces (React, Angular), microservices, and enterprise data sources for resilient, scalable solutions.
• Performance Tuning & MLOps: Apply best-in-class techniques for model performance, hyperparameter optimization, scalable retraining, monitoring, and CI/CD for AI pipelines.
• Research, Innovation & Thought Leadership: Stay at the cutting edge with constant exploration of new AI technologies—transforming foundational research into impactful product features.
• Standards Advocacy: Champion software engineering excellence—driving best practices in secure coding, peer review, and responsible AI design throughout the full SDLC.
• Cloud & Data Engineering Mastery: Develop and optimize cloud-native (AWS, GCP, Azure) AI workloads—utilizing Kubernetes, Docker, Spark, and high-performance data lakes for advanced data wrangling, batch and real-time inference, and model monitoring.
• Safety, Reliability, & Explainability: Set gold standards for AI safety, fairness, and explainability—implementing evaluation protocols, guardrails, and bias detection to ensure ethical agent behavior in real-world deployments.

You’ve got what it takes if you have…

• Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Machine Learning, or related field.
• 6+ years in software engineering , hands-on building, deploying, and optimizing AI/ML applications at enterprise scale.
• Advanced hands-on proficiency with cloud platforms (AWS, Azure, GCP), ML frameworks (TensorFlow, PyTorch, Hugging Face, Scikit-Learn), modern programming languages (Python, Java, Scala, C++), and distributed systems (Kubernetes, Docker, Spark).
• Strong foundation in system architecture, algorithm design, scalable data engineering (ETL, batch & stream processing), and model serving.
• Experience with modern MLOps, CI/CD, GitOps, and DevSecOps methodologies.
• Commitment to ethical, responsible AI—deep understanding of privacy, explainability, bias, and regulatory considerations.
  • Prior experience in HR tech, SaaS, or enterprise software highly advantageous.