About the Role
Haptiq is an AI-native enterprise solutions company helping organizations transform complex data, workflows, and business processes into scalable technology-driven operations. We're looking for an innovative AI Engineer to join our AI team and help design, prototype, and operationalize advanced AI systems, including Retrieval-Augmented Generation (RAG) pipelines, MCP services, and agentic workflows. You'll work closely with Python Developers and MLOps Engineers to bring AI-powered features from concept to production.
Responsibilities
•Design, prototype, and iterate on AI models, RAG pipelines, MCP services, and agentic reasoning systems.
•Develop AI-powered services using frameworks such as LangChain, LangGraph, CrewAI, LlamaIndex, and Haystack.
•Design, train, and evaluate machine learning models (supervised, unsupervised, and reinforcement learning) for real-world production use cases.
•Perform data preprocessing, feature engineering, and dataset curation to improve model performance and robustness.
•Experiment with model architectures, hyperparameter tuning, and optimization techniques to achieve performance targets.
•Implement model validation strategies, including cross-validation, A/B testing, and offline/online evaluation pipelines.
•Deploy, monitor, and maintain ML models in production, including handling model drift, retraining, and versioning.
•Build and optimize prompts for AI-driven tasks like chat, summarization, information extraction, and decision support.
•Integrate and tune vector databases (e.g., Chroma, FAISS, Pinecone, Qdrant) to optimize vector search workflows.
•Collaborate with Python Developers to define service interfaces, APIs, and operational requirements.
•Implement task decomposition, memory management, and tool-use strategies within agentic systems.
•Monitor AI system performance, operational risks, and prompt evaluation metrics.
•Stay current with emerging AI frameworks, architectures, and best practices in AI observability and evaluation.
Requirements
•Strong Python development skills with an emphasis on clean, scalable, and modular code.
•Hands-on experience working with LLMs and agent frameworks.
•Solid understanding of RAG architecture, vector search concepts (cosine similarity, MMR, late interaction), and system design.
•Experience building multi-step agentic workflows and reasoning systems.
•Familiarity with AI performance metrics, model drift detection, and operational monitoring.
•Awareness of AI limitations, including latency, hallucination risks, and explainability challenges.
•A curious, experimental mindset with a passion for testing emerging AI tools and frameworks.
•Strong foundation in machine learning fundamentals, including regression, classification, clustering, and model evaluation techniques.
•Experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn for model development and experimentation.
Nice to Have
•Experience with prompt optimization tools.
•Bachelor's degree in Computer Science or equivalent (preferred).
Conditions
We value creative problem solvers who learn fast, work well in an open and diverse environment, and enjoy pushing the bar for success ever higher. We do work hard, but we also choose to have fun while doing it.
