Senior AI/ML Engineer

Responsibilities

• Lead a team of AI/ML engineers, providing technical guidance, mentorship, and support.
• Collaborate directly with client teams to understand business problems, explain technical solutions clearly, and lead the implementation of AI/ML features while representing technical excellence.
• Lead the technical execution of AI/ML projects from experimentation to production.
• Translate business problems into AI/ML solutions and manage solution delivery.
• Guide team members on model selection, evaluation, fine‑tuning, and Gen AI usage.
• Collaborate with product, data engineering, and software teams for integrated delivery.
• Conduct code reviews, ensure best practices, and contribute to model deployment pipelines.
• Maintain documentation and model governance as per responsible AI standards.
• Define project goals, scope, and deliverables in collaboration with stakeholders.
• Create scalable architecture and robust machine learning systems that meet business objectives of a software product.
• Collaborate with cross‑functional teams to integrate machine learning solutions into existing products and workflows.
• Establish best practices and standards for machine learning development within the organization.
• Monitor and evaluate the performance of machine learning models in production and implement improvements as needed.
• Stay updated on industry trends and advancements in machine learning technologies.
• Implement, deploy, and optimize AI/ML algorithms and models into production environments.
• Conduct experiments, perform data analysis, and present findings to stakeholders.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field; a Master’s degree is preferred.
• 8+ years of experience in building AI models with ML, NLP, and deep learning.
• Proven track record of successfully delivering machine learning projects from concept to production.
• Strong leadership, interpersonal skills, and ability to effectively communicate technical concepts to non‑technical stakeholders.
• Excellent problem‑solving abilities and attention to detail.
• Strong knowledge of AI/ML techniques, algorithms, and frameworks. Experience building and deploying ML models (classification, NLP, forecasting).
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Strong programming skills in Python, and familiarity with libraries such as Py Torch, Tensor Flow, and Scikit‑Learn.
• Exposure to Matplotlib, Seaborn, or Plotly.
• Solid understanding of machine learning concepts, algorithms, and statistical methods.
• Experience with computer vision libraries (Open CV, etc.) and NLP libraries (spa Cy, Hugging Face, etc.).
• Knowledge of MLOps practices and experience with CI/CD tools.
• Good understanding of using LLMs via APIs or fine‑tuning open‑source models.
• Hands‑on with data prep (Pandas, Spark), feature engineering, model training and tuning.
• Knowledge of vector stores, embeddings, and LLMOps tools (Lang Chain, Llama Index, etc.).
• Experience with at least one cloud platform and CI/CD for ML (Git Hub Actions, MLflow, etc.).
• Exposure to monitoring frameworks (evidently. ai, whylogs, or custom dashboards).
• Proven ability to work independently and with a team.
• Extensive expertise in refining LLMs, implementing RAG architectures, Weaviate vector databases, and advanced prompt engineering.
• Led teams building Gen AI, LLM, or agentic AI applications.
• Experience building reusable AI frameworks or platforms.
• Experience with vector databases (FAISS, Pinecone), RAG, Hugging Face.
• Contributions to research papers, blogs, or open‑source projects.

We are an equal opportunity employer.

It goes without saying that we live by values like Intrinsic Dignity and Open Collaboration to create cutting‑edge technology and reinforce our commitment to diversity - globally and locally.

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