The Role
Lead the development and deployment of scalable AI and machine learning solutions using LLMs, NLP, neural networks, and modern MLOps practices. Build data pipelines, automate training and deployment workflows, optimize model performance, conduct data analysis and feature engineering, and integrate models into applications through APIs, microservices, and cloud platforms. Collaborate with data, product, and software engineering teams while tracking advancements in AI and generative AI.
Summary Generated by Built In
- Work
with LLMs, NLP models, neural networks, and other advanced AI
techniques.
- Build
scalable data pipelines and automate model training, validation, and
deployment workflows.
- Optimize
model performance, latency, and accuracy using modern techniques.
- Work
closely with cross-functional teams including Data Engineering, Product,
and Software Engineering.
- Perform
exploratory data analysis, feature engineering, and experiment tracking.
- Integrate
AI models into applications using APIs, microservices, or cloud-based
solutions.
- Stay updated with the latest advancements in AI,
ML, and GenAI technologies.
Requirements
- 10+ years of hands-on experience in AI/ML engineering.
- Strong
proficiency in Python, ML libraries (NumPy, Pandas, Scikit-learn),
and DL frameworks (TensorFlow/PyTorch).
- Experience
with LLMs, NLP, transformers, embeddings, and prompt optimization.
- Knowledge
of MLOps tools like MLflow, Kubeflow, Airflow, or similar.
- Experience
in using cloud platforms (AWS / Azure / GCP) for AI model
deployment.
- Hands-on
experience with vector databases, feature stores, or model
registries (nice to have).
- Familiarity
with API development, microservices architecture, and
containerization (Docker, Kubernetes).
- Strong
understanding of algorithms, data structures, and model evaluation
techniques.
Skills Required
- 10+ years of hands-on experience in AI/ML engineering
- Strong proficiency in Python, NumPy, Pandas, and Scikit-learn
- Experience with TensorFlow or PyTorch
- Experience with LLMs, NLP, transformers, embeddings, and prompt optimization
- Knowledge of MLOps tools such as MLflow, Kubeflow, or Airflow
- Experience using AWS, Azure, or GCP for AI model deployment
- Hands-on experience with vector databases, feature stores, or model registries
- Familiarity with API development, microservices architecture, Docker, and Kubernetes
- Strong understanding of algorithms, data structures, and model evaluation techniques
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The Company
What We Do
InFynd is a cloud-based B2B data and prospecting platform that helps sales, marketing, and recruiting teams identify and engage potential customers. It provides contact information, company profiles, buyer-intent data, social media handles, and verified business email addresses and phone numbers. The platform supports target identification, company research, lead generation, and more effective sales conversion through accurate, GDPR-compliant business intelligence.
