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EdCast is seeking a Senior AI Engineer to design, build, and deploy sophisticated AI-driven features and autonomous agents in an enterprise SaaS environment. This is a hands-on role focused on full-stack AI engineering, from development through production optimization.
Key Responsibilities:
- Develop and deploy AI-native products and intelligent autonomous agents (LLMs, multi-agent systems) capable of planning, reasoning, and decision-making to solve complex HR and talent management challenges.
- Design and build frameworks for multi-agent orchestration, including message passing, prompt engineering, vector databases (FAISS, Pinecone), and scalable knowledge graphs.
- Integrate agentic and generative AI systems with modern APIs (REST/gRPC), microservices, user interfaces (React, Angular), and enterprise data sources.
- Apply MLOps best practices: model performance tuning, hyperparameter optimization, scalable retraining, monitoring, and CI/CD for AI pipelines.
- Develop and optimize cloud-native AI workloads on AWS, GCP, or Azure using Kubernetes, Docker, Spark, and high-performance data lakes for batch and real-time inference.
- Implement AI safety, fairness, and explainability standards—evaluation protocols, guardrails, and bias detection to ensure ethical agent behavior.
- Stay current with cutting-edge AI research and translate foundational advances into impactful product features.
- Champion software engineering excellence through secure coding, peer review, and responsible AI design across the full software development lifecycle.
Requirements:
- Bachelor's, Master's, or PhD in Computer Science, Engineering, Machine Learning, or related field.
- 6+ years of software engineering experience with hands-on building, deploying, and optimizing AI/ML applications at enterprise scale.
- Advanced proficiency with cloud platforms (AWS, Azure, GCP), ML frameworks (TensorFlow, PyTorch, Hugging Face, Scikit-Learn), and modern programming languages (Python, Java, Scala, C++).
- Strong foundation in distributed systems (Kubernetes, Docker, Spark), system architecture, algorithm design, and scalable data engineering (ETL, batch and stream processing).
- Experience with model serving, modern MLOps, CI/CD, GitOps, and DevSecOps methodologies.
- Deep understanding of ethical AI principles: privacy, explainability, bias detection, and regulatory considerations.
- Prior experience in HR tech, SaaS, or enterprise software is highly advantageous.
