Job Description
Welcome to the future. At Apex Future Systems, we are not just building software; we are architecting the intelligence layer for the year 2026. We are seeking a visionary Senior AI Engineer to lead our Agentic AI initiative. In this role, you will define how autonomous agents will interact with the digital economy, creating self-sustaining systems capable of complex reasoning and decision-making.
If you are passionate about pushing the boundaries of Generative AI, fine-tuning LLMs for enterprise-grade autonomy, and solving the hardest problems in Machine Learning, we want to meet you.
Responsibilities
- Architect Autonomous Agents: Design and implement multi-agent systems that can autonomously plan, execute, and learn from complex workflows without human intervention.
- Model Optimization: Lead the research and deployment of next-generation Large Language Models (LLMs), focusing on efficiency, accuracy, and reduced hallucination rates.
- Evaluation Pipelines: Build rigorous, automated evaluation frameworks to measure the performance and safety of AI agents in production environments.
- System Integration: Integrate AI capabilities into legacy infrastructure, ensuring seamless interoperability and scalability.
- Ethical AI Governance: Establish and enforce best practices for AI safety, bias mitigation, and responsible AI usage within the organization.
- Research Collaboration: Collaborate with a team of data scientists and engineers to prototype cutting-edge algorithms for the 2026 roadmap.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Artificial Intelligence, or a related technical field.
- Experience: 5+ years of professional experience in Machine Learning, Deep Learning, or Natural Language Processing (NLP).
- Technical Skills: Proficiency in Python, PyTorch, or TensorFlow. Deep understanding of Transformer architectures and attention mechanisms.
- Agentic AI: Demonstrated experience in designing autonomous agents, tool-use capabilities, or RAG (Retrieval-Augmented Generation) systems.
- Production Experience: Proven track record of deploying ML models into high-scale, production environments (AWS, GCP, or Azure).
- Communication: Excellent ability to communicate complex technical concepts to non-technical stakeholders and cross-functional teams.