Senior AI Engineer

BigTapp Analytics; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Benefits offered by this job

Health insurance

Parental insurance

Retirement savings plan

Job summary

BigTapp Analytics in Bangalore seeks a Senior AI Engineer to design, develop, and operate production-grade AI, ML, and GenAI solutions from concept to deployment. You will own end-to-end pipelines, APIs, and scalable architectures, collaborating with data, platform, and business teams.

The role requires deep hands-on expertise with Python, MLOps, and cloud platforms, plus a proven track record of taking AI projects through production with measurable impact.

Qualifications

• 7+ years of software/AI/ML engineering experience.
• 3+ years building AI/GenAI solutions in production.
• Proven track record taking AI from concept to production.

Responsibilities

• Design end-to-end AI/ML/GenAI solutions for production.
• Translate business problems into scalable AI architectures and plans.
• Develop production-ready applications, APIs, and data pipelines.
• Build data pipelines for model training, inference, evaluation, and monitoring.

Skills

Python

AI/ML

GenAI

LLMs

Docker

Kubernetes

Cloud platforms

Education

Bachelor's or Master's in CS/DS

Tools

FastAPI

PyTorch

TensorFlow

MLflow

LangChain

HuggingFace

Job description

Job Description – Senior AI Engineer

Location: Bangalore

Experience: 7+ Years

Job Summary

BigTapp is seeking an experienced Senior AI Engineer with strong hands-on expertise in designing, developing, deploying, and operating production-grade AI, Machine Learning, and Generative AI solutions. The ideal candidate should have proven experience taking AI solutions from concept to production, including architecture, model development, data pipelines, APIs, deployment, monitoring, and continuous optimization. The role requires strong engineering skills across the complete AI lifecycle, along with the ability to work closely with business stakeholders, data teams, engineering teams, and platform teams to deliver scalable and measurable AI solutions.

Mandatory Skills

• 7+ years of overall experience in Software Engineering, Data Engineering, AI Engineering, or Machine Learning Engineering.
• 3+ years of hands-on experience building and deploying AI/ML or GenAI solutions for real-world business use cases.
• Proven experience taking AI solutions from concept to production.
• Strong programming skills in Python with experience in FastAPI, Flask, PyTorch, TensorFlow, Scikit-learn, or equivalent frameworks.
• Hands-on experience with LLMs, RAG, prompt engineering, embeddings, vector search, and AI orchestration frameworks.
• Experience with LangChain, LlamaIndex, MLflow, OpenAI/Azure OpenAI, Anthropic, Hugging Face, or equivalent platforms.
• Strong understanding of data pipelines, feature engineering, model evaluation, inference pipelines, and model monitoring.
• Experience with Docker, Kubernetes, CI/CD, and cloud platforms such as AWS, Azure, or GCP.
• Strong understanding of APIs, microservices, event-driven architecture, and production-grade system design.
• Strong problem-solving skills with the ability to work with ambiguous business requirements.

Key Responsibilities

• Design and develop end-to-end AI, Machine Learning, and Generative AI solutions from concept through production.
• Translate complex business problems into scalable AI architectures and technical implementation plans.
• Develop production-ready AI applications, APIs, backend services, workflows, and user-facing AI capabilities.
• Build data pipelines required for model training, inference, evaluation, and monitoring.

GenAI & LLM Engineering

• Develop LLM-based solutions using RAG, prompt engineering, embeddings, vector databases, and agentic workflows.
• Evaluate, fine-tune, and optimize AI models for accuracy, latency, cost, reliability, and business impact.
• Build AI agents, intelligent assistants, workflow automation, and decision-support solutions where applicable.

Deployment & Production Engineering

• Deploy AI workloads using Docker, Kubernetes, CI/CD pipelines, and cloud platforms.
• Implement MLOps/LLMOps practices for reliable model deployment and lifecycle management.
• Monitor production AI systems for model performance, drift, hallucination risks, failures, cost anomalies, and user adoption.
• Implement appropriate logging, monitoring, testing, and operational support processes.

Security & Governance

• Design secure and governed AI solutions with appropriate access controls, data privacy, auditability, and responsible AI practices.
• Ensure AI solutions follow enterprise security, compliance, and governance requirements.

Stakeholder & Team Collaboration

• Collaborate with Product Owners, Data Engineers, Software Engineers, Architects, and business stakeholders to deliver measurable outcomes.
• Create architecture diagrams, technical documentation, deployment guides, and operational runbooks.
• Mentor junior engineers and promote best practices in AI engineering, testing, code quality, and production operations.

Qualification

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Engineering, Mathematics, or a related discipline.
• Equivalent hands-on industry experience may also be considered.

Technical Skills

• Strong proficiency in Python with experience developing scalable and maintainable AI applications and services.
• Hands-on experience with PyTorch, TensorFlow, Scikit-learn, FastAPI, Flask, or equivalent AI/ML frameworks.
• Strong understanding of LLMs, RAG architectures, prompt engineering, embeddings, vector databases, and AI agents.
• Experience with LangChain, LlamaIndex, MLflow, OpenAI/Azure OpenAI, Anthropic, Hugging Face, or equivalent technologies.
• Strong experience in data pipelines, feature engineering, model evaluation, inference pipelines, and model monitoring.
• Experience deploying production applications using Docker, Kubernetes, CI/CD and cloud platforms.
• Strong knowledge of AWS, Azure, or GCP and their AI/ML services.
• Experience designing and developing REST APIs, microservices, and event-driven architectures.
• Experience working with databases, data lakes, data warehouses, and vector databases.
• Ability to develop clean, modular, testable, secure, and maintainable production code.

Soft Skills

• Strong analytical and problem-solving abilities.
• Excellent communication and stakeholder management skills.
• Ability to translate complex business requirements into practical technical solutions.
• Strong ownership and delivery mindset.
• Ability to work independently and collaboratively across technical and business teams.
• Strong mentoring and knowledge-sharing capabilities.

Good to Have

• Experience with Databricks, Snowflake, Azure ML, Amazon SageMaker, Vertex AI, or similar enterprise AI/data platforms.
• Experience implementing MLOps, LLMOps, model governance, and responsible AI practices.
• Experience with real-time or near-real-time AI inference systems.
• Exposure to Aviation, Logistics, Supply Chain, Workforce Planning, Customer Operations, or Enterprise Analytics.
• Experience optimizing GenAI solutions for cost, latency, accuracy, and reliability.
• Familiarity with observability, logging, tracing, model monitoring, and production support tools.

Work Experience

• 7+ years of overall experience in Software Engineering, Data Engineering, AI Engineering, or Machine Learning Engineering.
• Minimum 3+ years of hands-on experience developing and deploying AI/ML or GenAI solutions.
• Proven track record of delivering production-grade AI systems, rather than only prototypes or POCs.
• Experience owning AI initiatives across architecture, engineering, data, model development, deployment, monitoring, and continuous improvement.

Compensation & Benefits

• Competitive salary and annual performance-based bonuses.
• Comprehensive health and optional Parental insurance.
• Optional retirement savings plans and tax savings plans.

KRA (Key Result Areas)

• Successful design and delivery of production-grade AI/ML and GenAI solutions.
• Scalable and reliable AI application architecture and deployment.
• Model performance, accuracy, reliability, and continuous optimization.
• Effective implementation of MLOps/LLMOps, monitoring, security, and governance.
• Successful collaboration with business and technical stakeholders.

KPI (Key Performance Indicators)

• Successful production deployment of AI solutions within agreed timelines.
• Model accuracy, reliability, latency, and uptime against defined targets.
• Reduction in AI-related production incidents and model failures.
• Improvement in AI solution cost and processing efficiency.
• Stakeholder adoption and measurable business impact of delivered AI solutions.

Contact: [e-mail]