Senior AI Engineer:We're looking for a curious, motivatedSenior AI Engineer to join our team and help build the next generation of intelligenttalent technology. This is a hands-on opportunity to learn how AI and machine learning aredeveloped and deployed in a real-world, enterprise SaaS environment. You'll workalongside experienced engineers to build AI native products, AI Agents and explorehow large language models, recommender systems, and other AI tools can makework more personal and efficient.
In this role you will...- 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 biasdetection 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.
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