AI/ML Engineer

Full time Full day

Afiniti is the world’s leading applied artificial intelligence and advanced analytics provider. Afiniti Enterprise Behavioral Pairing uses artificial intelligence to identify subtle and valuable patterns of human interaction in order to pair individuals on the basis of behavior, leading to more successful interactions and measurable increases in enterprise profitability. Afiniti operates throughout the world, and has measurably driven billions of dollars in incremental value for our clients


As a part of the NLP team in Engineering, you will develop and implement NLP solutions across a range of projects and initiatives, such as information retrieval systems, conversational AI, and dialog semantics from text and voice data


  • Design and develop AI/NLP models to extract insights from large text datasets using Python, NLTK, and other NLP frameworks.

  • Develop and maintain production-level NLP pipelines to support business applications.

  • Work with cross-functional teams to deliver end-to-end AI/NLP solutions that solve real-world business problems.

  • Implement MLOps best practices for model management, monitoring, and deployment.

  • Work closely with data scientists, engineers, and product managers to understand requirements and deliver solutions that meet business objectives.

  • Continuously improve and optimize models through experimentation and data analysis.

  • Stay up-to-date with the latest NLP and ML research and technologies.

  • Able to manage small project teams and work independently when necessary.

  • Collaborate with other teams and stakeholders to develop and implement NLP strategies.


  • Fresh to 3 years of relevant work experience in AI/NLP development

  • Proficient in Python and have exposure working with ML/NLP frameworks.

  • Proficient in technologies such as speech-to-text, conversational AI, transformer architectures, LSTMs, BERT, and CNNs using frameworks like PyTorch, MapReduce framework, MongoDB, Apache Spark, and cloud computing (AWS, Google Cloud, Openstack, Terraform, Ansible, Jenkins, Docker, Terraform, etc.).

  • Strong background in data science workflow using Apache Spark for exploratory data analysis, PyTorch & PyTorch-Lightning for distributed training, and Weights & Biases for experiment logging and performance monitoring.

  • Experience with MLOPS workflow using tools such as Docker builds, automated unit & acceptance testing (for model APIs, inputs/outputs), Apache AirFlow, and BitBucket automated builds.

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