AI Engineer

Key Responsibilities:

• Design and implement traditional ML and LLM-based systems and applications.
• Optimize model inference for performance and cost-efficiency.
• Fine-tune foundation models using methods like LoRA, QLoRA, and adapter layers.
• Develop and apply prompt engineering strategies including few-shot learning, chain-of-thought, and RAG.
• Build robust backend infrastructure to support AI-driven applications.
• Implement and manage MLOps pipelines for full AI lifecycle management.
• Design systems for continuous monitoring and evaluation of ML and LLM models.
• Create automated testing frameworks to ensure model quality and performance.

Basic Qualifications:

• Bachelor’s degree in Computer Science, AI, Data Science, or a related field.
• 4+ years of experience in AI/ML engineering, software development, or data-driven solutions.
• LLM Expertise
• Experience with parameter-efficient fine-tuning (LoRA, QLoRA, adapter layers).
• Understanding of inference optimization techniques: quantization, pruning, caching, and serving.
• Skilled in prompt engineering and design, including RAG techniques.
• Familiarity with AI evaluation frameworks and metrics.
• Experience designing automated evaluation and continuous monitoring systems.
• Backend Engineering
• Strong proficiency in Python and frameworks like FastAPI or Flask.
• Experience building RESTful APIs and real-time systems.
• Knowledge of vector databases and traditional databases.
• Hands-on experience with cloud platforms (AWS, GCP, Azure) focusing on ML services.
• MLOps & Infrastructure
• Familiarity with model serving tools (vLLM, SGLang, TensorRT).
• Experience with Docker and Kubernetes for deploying ML workloads.
• Ability to build monitoring systems for performance tracking and alerting.
• Experience building evaluation systems using custom metrics and benchmarks.
• Proficient in CI/CD and automated deployment pipelines.
• Experience with orchestration tools like Airflow.

• Hands-on experience with LLM frameworks (Transformers, LangChain, LlamaIndex).
• Familiarity with LLM-specific monitoring tools and general ML monitoring systems.
• Experience with distributed training and inference on multi-GPU environments.
• Knowledge of model compression techniques like distillation and quantization.
• Experience deploying models for high-throughput, low-latency production use.
• Research background or strong awareness of the latest developments in LLMs.

• Tools & Technologies We Use
• Frameworks: PyTorch, TensorFlow, Hugging Face Transformers
• Serving: vLLM, TensorRT-LLM, SGlang, OpenAI API
• Infrastructure: Docker, Kubernetes, AWS, GCP
• Databases: PostgreSQL, Redis, Vector Databases