Machine Learning Engineer

Join our team in building Context API, a SaaS product designed to turn raw, unstructured content into powerful, searchable metadata. Whether it’s a 200-page legal document, an image, a video, or an audio recording, Context Enrichment uses both GenAI and PredictiveAI techniques to generate summaries, topics, named entities, tags, and classifications to enhance discoverability, automation, and insight generation.

You'll contribute to advanced NLP and multimodal AI pipelines that power intelligent metadata extraction for customers across key industries such as healthcare, finance, and insurance.

As a Machine Learning Engineer, you’ll play a key role in scaling our AI capabilities and shaping the intelligence behind Context Enrichment. Your work will directly impact the user experience, performance, and trustworthiness of our enrichment engine. You will work in close collaboration with cross-functional teams, including those developing AI Agents and managing our centralized Data Lake, to ensure seamless integration and alignment across the platform.

• Run experiments with LLMs, open-source models, and prompt optimization for summarization, classification, entity recognition and other metadata extraction

• Improve response quality by integrating scoring, reasoning, and evaluation metrics

• Build and maintain a framework for evaluation and model comparison

• Designing and implementing production-grade data pipelines to transform raw data into structured, enriched outputs

• Collaborate with product, engineering, and vertical teams (e.g., healthcare, finance, insurance) to align ML features with business use cases

• Participate in fine-tuning LLMs and domain-specific model optimization

• • 3+ years of experience in ML/NLP/GenAI development

  • Hands-on experience with LLMs, prompt engineering, and ML platform frameworks

  • Proficiency in Python and frameworks like Hugging Face, PyTorch, or TensorFlow

  • Familiarity with both relational and non-relational databases, vector databases, and graph databases (e.g., PostgreSQL, MongoDB, Pinecone, ElasticSearch).

  • Strong programming skills in Python and SQL, with experience in at least one data processing framework (e.g., Panda/Dask, Spark, Flink, Beam, Kafka …).

  • Experience with cloud-based ML (preferably AWS: SageMaker, Bedrock, Lambda, S3)

  • Knowledge of evaluation techniques, including scoring and benchmarking models

  • Understanding of ML Ops concepts and tools (e.g., MLflow, Weights & Biases)

  • Knowledge of machine learning concepts and experience applying them in real-world use cases.

  • Ability to package and deploy code to production environments in the Cloud.

  • Experience with monitoring and observability tools (e.g. Datadog, Grafana, Prometheus).

  • Ability to use original thinking to translate goals into the implementation of new ideas and design solutions

  • Experience with data engineering, including working with data warehouses, ETL

  • Knowledge of CI/CD tools like Terraform and Docker