Senior Full Stack AI/ML Engineer

Paxcom; Gurugram District; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Paxcom is looking for a Senior Full Stack AI/ML Engineer to integrate advanced AI solutions within enterprise environments. The role involves using a solid foundation in full-stack development with expertise in Machine Learning and Generative AI to design robust applications.

Candidates with experience deploying production-ready AI models and strong knowledge of REST APIs and microservices architecture are encouraged to apply.

Qualifications

• 4+ years of experience in Machine Learning, Data Science, or AI with prior full-stack development experience.
• Experience deploying ML models into production systems at scale.
• Solid understanding of microservices architecture and REST APIs.

Responsibilities

• Design, develop, and deploy scalable AI/ML solutions integrated with enterprise systems.
• Build and operationalize machine learning models for high-volume applications.
• Integrate ML/AI models into microservices and RESTful APIs.

Skills

Machine Learning

Full Stack Development

Data Science

REST APIs

Microservices Architecture

GenAI Models

Tools

AWS

Linux

ML Libraries (NumPy, SciPy, etc.)

Job description

Job Title: Senior Full Stack AI/ML Engineer (GenAI & Enterprise Systems)

Job Type: Full Time | Work from Office

Job Location: Mohali/Gurugram

Role Overview:

We are seeking an experienced Full Stack AI/ML Engineer to design and deliver enterprise-grade intelligent applications. This role requires a strong foundation in full-stack development (MEAN/MERN/Java) combined with expertise in Machine Learning and Generative AI. The candidate will be responsible for building scalable, secure, and production-ready AI solutions that integrate seamlessly with enterprise systems and workflows.

The ideal candidate is someone who has transitioned from a full-stack development background into Machine Learning/AI roles over the past years. They bring a strong foundation in software development along with hands‑on experience in building, deploying, and scaling AI/ML and Generative AI solutions within enterprise environments.

Key Responsibilities:

• Design, develop, and deploy scalable AI/ML solutions integrated with enterprise‑grade web and backend systems.
• Build and operationalize machine learning models for real‑world, high‑volume business applications.
• Develop and maintain Retrieval‑Augmented Generation (RAG) pipelines leveraging enterprise data sources.
• Integrate ML/AI models into microservices‑based architectures and RESTful APIs.
• Collaborate with cross‑functional teams including Product, Data Engineering, DevOps, and Security to deliver robust solutions.
• Ensure compliance with enterprise standards for security, data governance, and privacy.
• Monitor, evaluate, and continuously improve model performance in production environments.
• Implement logging, monitoring, and alerting for ML systems to ensure reliability and observability.
• Contribute to architectural decisions and technology strategy for AI/ML initiatives.

Required Skills & Experience:

• 4+ years of experience in Machine Learning, Data Science, or AI, with prior full‑stack development experience in enterprise environments.
• Proven experience deploying ML models into production systems at scale.
• Solid understanding of microservices architecture, REST APIs, and distributed systems.
• Experience with GenAI models (e.g., GPT, LLaMA) and prompt engineering in enterprise use cases.
• Proficiency with ML libraries such as NumPy, SciPy, Scikit‑learn, and Matplotlib.
• Hands‑on experience with Linux‑based systems.
• Experience with cloud platforms such as AWS (EC2, S3, ECR, Lambda) or equivalent.
• Strong understanding of machine learning algorithms, model evaluation, and lifecycle management.

Preferred / Advanced Skills:

• Experience with deep learning frameworks and architectures (CNNs, RNNs, Transformers, LSTMs).
• Hands‑on experience with NLP models (BERT, GPT, T5, XLNet).
• Experience designing and deploying RAG‑based solutions using vector databases (e.g., FAISS, Pinecone).
• Familiarity with model optimization tools such as CUDA, ONNX, or TensorRT.
• Experience with distributed computing frameworks (e.g., Spark, Ray).
• Knowledge of data engineering pipelines and large‑scale data processing.

Enterprise‑Focused Competencies:

• Experience working with large‑scale, high‑availability systems handling significant traffic and data volumes.
• Understanding of enterprise security practices, including authentication, authorization, and data protection.
• Familiarity with governance, compliance, and audit requirements in regulated environments.
• Experience implementing CI/CD pipelines, containerization (Docker), and orchestration (Kubernetes).
• Knowledge of MLOps practices, including model versioning, monitoring, and lifecycle management.

Soft Skills:

• Strong analytical and problem‑solving capabilities.
• Ability to drive technical decisions and influence architecture in a cross‑functional environment.
• Effective stakeholder communication, including with non‑technical audiences.
• Ability to work in structured, process‑driven enterprise environments.