Haystek Technologies
Website:
haystek.com
Job details:
Sr. Machine Learning Engineer
About the Role:We are looking for an ML Engineer Lead Design and build the machine learning and data foundations that power our AI platform. While AI Developers create intelligent applications, ML Engineers ensure those applications are accurate, scalable, and continuously improving.
You will design and optimize embedding pipelines, retrieval systems, evaluation frameworks, and data pipelines that enable high-performing AI solutions. This role is ideal for someone who enjoys solving data-intensive problems, measuring model performance, and using experimentation to improve AI systems.
As AI continues to evolve, you will continuously evaluate new models, embedding techniques, retrieval strategies, and ML approaches to ensure our platform remains accurate, efficient, and future-ready.
Responsibilities:- Design, build, and maintain machine learning pipelines supporting AI applications.
- Develop and optimize embedding pipelines, vector databases, and Retrieval-Augmented Generation (RAG) infrastructure.
- Build evaluation frameworks to measure retrieval quality, response accuracy, relevance, latency, and overall AI system performance.
- Improve search relevance through chunking strategies, reranking, metadata filtering, and retrieval optimization.
- Analyze production data to identify opportunities for improving AI quality and user experience.
- Design experiments, evaluate results, and make data-driven recommendations.
- Develop monitoring and observability for AI systems, including model performance, embeddings, retrieval metrics, and data quality.
- Collaborate with AI Developers to improve chatbot accuracy and reliability.
- Evaluate emerging ML techniques, embedding models, and AI infrastructure technologies for business adoption.
- Establish best practices for ML engineering, experimentation, and model lifecycle management.
Experience & Qualifications:- 4 - 6 years of experience in Machine Learning, Applied AI, Data Science, or Software Engineering with a focus on ML systems.
- Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related quantitative field. A Master’s degree is a plus.
- Strong programming skills in Python and SQL.
- Strong understanding of machine learning fundamentals, statistics, and experimental design.
- Experience with machine learning libraries such as PyTorch, TensorFlow, or Scikit-learn.
- Hands-on experience with embeddings, vector databases, Retrieval-Augmented Generation (RAG), feature engineering, and model evaluation.
- Experience building data pipelines and integrating structured and unstructured datasets.
- Familiarity with cloud platforms (preferably AWS), Docker, Git, and modern development practices.
- Strong analytical mindset with experience designing experiments and interpreting model performance metrics.
- Demonstrated passion for continuous learning through research, personal projects, open-source contributions, or experimentation with emerging ML and AI technologies.
Nice to Have:- Experience with pgvector, Pinecone, Weaviate, FAISS, Milvus, or similar vector databases.
- Experience with MLflow, Weights & Biases, LangSmith, Arize, Phoenix, or other evaluation and observability platforms.
- Knowledge of ranking algorithms, recommendation systems, semantic search, or information retrieval.
- Experience with distributed data processing using Spark, Airflow, or similar orchestration tools.
- Familiarity with LLMOps, model deployment, inference optimization, and production monitoring.
What We Are Looking For:We are looking for engineers who:
- Experience working with large-scale structured and unstructured datasets for search, recommendation or analytics.
- Enjoy solving data and machine learning problems through experimentation and analysis.
- Think in terms of measurement, evaluation, and continuous improvement.
- Are passionate about optimizing AI performance rather than simply integrating AI models.
- Stay current with advances in machine learning, retrieval systems, embeddings, and model evaluation.
- Build reliable, scalable ML systems that directly improve the quality of AI applications.
Click on Apply to know more.
