AI Engineer

Key Responsibilities

• AI Feature Development: Design, fine-tune, and deploy large language models (LLMs), RAG (Retrieval-Augmented Generation) systems, and machine learning workflows to enhance candidate discovery and automated job matching.
• Pipeline & Architecture: Build robust, scalable data ingestion and embedding pipelines to process structured and unstructured talent data (resumes, job postings, market signals).
• System Optimization: Optimize inference performance, latency, and API costs across production AI microservices.
• Evaluation & Benchmarking: Establish continuous evaluation metrics for model accuracy, hallucination monitoring, and bias reduction in hiring tools.
• Cross-Functional Collaboration: Partner closely with product management and platform engineers to integrate AI capabilities seamlessly into web applications and user workflows.

Qualifications & Skills

Required:

• Experience: 3+ years of software engineering experience with a strong focus on building and deploying ML/AI applications into production.
• Languages & Frameworks: High proficiency in Python and deep experience with AI frameworks (PyTorch, TensorFlow, LangChain, LlamaIndex).
• GenAI & Vector Search: Proven experience with modern LLMs (OpenAI, Anthropic, open-source models like Llama) and Vector Databases (Pinecone, Weaviate, Qdrant, Chroma).
• Engineering Best Practices: Strong background in RESTful APIs, asynchronous processing, Docker, microservices, and cloud infrastructure (AWS/GCP/Azure).
• Data Processing: Experience working with structured SQL and NoSQL databases, data modeling, and text processing (NLP).

Preferred / Bonus:

• Prior experience building HR-tech, job search, or candidate-matching platforms.
• Experience with fine-tuning open-source LLMs or fine-grained prompt engineering strategies.
• Knowledge of search technology (Elasticsearch, OpenSearch, semantic search architecture).