AI/ML Engineer

Position Summary

As a AI/ML Engineer, you will design, build, and deploy advanced AI systems with a focus on agentic automation, LLM-powered workflows, and scalable machine learning in production. You will own end-to-end delivery of complex AI features: from data preparation and model development to orchestration, deployment, and ongoing optimization.

You will collaborate closely with R&D and software engineering teams to integrate AI capabilities into Alpha X's core products. As a senior individual contributor, you are expected to contribute to architectural decisions, uphold engineering excellence, and mentor junior team members.

Key Responsibilities

AI & Model Engineering:

• Develop, fine-tune, and evaluate ML and LLM models for real-world production use cases.
• Build systems involving retrieval-augmented generation (RAG), agentic workflows, function-calling pipelines, and multimodal reasoning.
• Explore and integrate frontier AI techniques, including tool-using LLMs, self-correcting agent loops, evaluation frameworks and orchestration patterns.

Data & Pipeline Engineering:

• Design and build robust data pipelines (ETL/ELT) to support model training, inference, and continuous learning.
• Implement data validation, observability, and quality controls for reliable end-to-end AI operation.

AI Orchestration & Platform Integration:

• Develop AI workflows using modern agentic (e.g., LangChain, OpenAI Responses API) and orchestration frameworks (e.g., LangGraph, LlamaIndex, Ray, Airflow).
• Integrate AI components into Alpha X's application stack in close collaboration with software engineering.
• Ensure systems are modular, maintainable, and scalable.

Deployment & MLOps:

• Deploy models in cloud platforms (e.g., AWS) with strong emphasis on performance, reliability, and cost efficiency.
• Manage CI/CD for ML, including model versioning, monitoring, evaluation, and retraining.
• Containerize workloads using Docker and orchestrate infrastructure with Kubernetes.

Performance Optimization:

• Improve accuracy, inference speed, memory efficiency, and hardware utilization.
• Apply advanced optimization techniques (quantization, distillation, pruning).

Technical Leadership:

• Lead design and implementation of medium-to-large AI systems with high autonomy.
• Provide architectural guidance and review code, pipelines, and model designs from other engineers.
• Establish and champion best practices for prompt engineering, testing, documentation, and model evaluation.
• Identify technical risks early and drive mitigation strategies.

Project Ownership:

• Drive end-to-end delivery of complex AI features and improvements.
• Translate ambiguous business problems into actionable AI solutions.
• Collaborate with PMs, stakeholders, and cross-functional teams to align requirements and technical plans.

Mentorship:

• Mentor junior engineers in ML techniques, model development, and engineering best practices.
• Contribute to internal knowledge sharing through documentation, demos, and technical discussions.

Other Duties: Perform any ad-hoc duties assigned by the Company as required.

Qualifications & Skills

Required:

• Bachelor's or Master's degree in Computer Science, Mathematics,Data Science, Engineering, or a related field.
• At least 5 years of relevant experience in appliedmachine learning or AI engineering.
• Expert proficiency in Python and ML frameworks (e.g., PyTorch,TensorFlow, JAX, or Keras).
• Strong understanding of LLM fundamentals: prompting,fine-tuning, embeddings, vector databases, RAG, and agent architectures.
• Experience with agentic AI systems, multi-agentorchestration, or tool-using LLM pipelines.
• Solid grounding in linear algebra, calculus,statistics, and probability.
• Experience with SQL/NoSQL databases and big datasystems (e.g., Spark, Kafka).
• Hands-on experience with cloud ML platforms (AWSBedrock/SageMaker, Vertex AI or Azure ML) and containerization (Docker,Kubernetes).
• Proficiency with Git and modern collaborative development workflows.

Preferred/ Additional:

• Demonstrated experience deploying at least one significant ML/LLM system into production.
• Familiarity with distributed inference, GPU optimization, or vector-first data architectures (e.g.,Weviate, Milvus).
• Ability to break down complex problems into modularengineering tasks.
• Strong communication skills for explaining complex AI concepts to non-technical stakeholders.
• Comfortable working in a fast-paced environment with evolving priorities.