Artificial Intelligence/Machine Learning Engineer

Position : AI ML Engineer

Experience : 7 years in Python development with proven experience in AI and productivity tooling

Work mode : Hybrid/Remote

NP : Immediate joiners

Job Mode : Contract(6 months extendable)

Working hours : 2pm - 12pm IST

Job Summary :

We are seeking a Senior Python Developer who excels at building scalable, AI-integrated systems using modern tools and frameworks.

The ideal candidate embraces AI-assisted development (GitHub Copilot, ChatGPT, AutoGen, etc.) to boost productivity, improve code quality, and drive innovation across our application stack.

This role combines backend expertise, cloud-native architecture, and hands-on AI integration to deliver intelligent, high-performance solutions.

Key Responsibilities :

AI Model Integration & Optimization :

- Integrate APIs from multiple AI platforms (OpenAI, Anthropic, Gemini, Llama, Mistral, etc.) into scalable backend systems.

- Build multi-model orchestration layers balancing cost, latency, and accuracy.

- Fine-tune prompts, manage context windows, and implement RAG (Retrieval-Augmented Generation) solutions for domain-specific use cases.

- Optimize token usage, caching, and filtering strategies to enhance system efficiency and user experience.

Application & System Development :

- Design and implement AI-enabled workflows seamlessly integrated with web, mobile, or enterprise ecosystems.

- Develop Python-based backends and APIs using frameworks like FastAPI, Flask, or Django.

- Build and deploy microservices and cloud-native services leveraging Docker, Kubernetes, and serverless architectures

- Collaborate with frontend, DevOps, and product teams to ensure smooth feature delivery and deployment.

- Monitor and evaluate AI responses through metrics, evaluation frameworks, or RLHF-inspired feedback loops.

- Implement AI guardrails for responsible usage including bias detection, toxicity filtering, and compliance enforcement.

- Debug and resolve performance or reliability issues in AI-powered production systems.

Innovation & Collaboration :

- Stay up to date with the evolving AI model landscape, exploring new models, APIs, and orchestration frameworks.

- Experiment with multi-modal AI (vision, text, speech) for applicability in client scenarios.

- Work closely with cross-functional teams to translate business goals into intelligent, automated features.

Primary Skills :

- Python backend expert : FastAPI, async I/O, API design, testing.

- Production LLM integration : OpenAI/Anthropic/Gemini/Mistral; prompt and context strategies; RAG with a vector DB.

- Cloud-native delivery : Docker, AWS (preferred), CI/CD, IaC basics (Terraform or Pulumi).

- Data layer : SQL (PostgreSQL), caching/queues (Redis Celery/RQ/SQS/Kafka).

- Daily AI-assisted development (Copilot/Others) for coding and tests.

Required Skills & Qualifications :

- Expert in Python backend development with hands-on experience integrating AI models, building cloud-native microservices, and using AI-assisted coding tools for faster, smarter development.

- Proven hands-on experience integrating LLM APIs (OpenAI, Claude, Gemini, Llama, etc.).

- Strong expertise in AI/ML frameworks (TensorFlow, PyTorch, scikit-learn, Hugging Face, etc.)" as essential qualification

- Practical knowledge of LangChain, LlamaIndex, Codium or similar frameworks for AI workflow orchestration.

- Understanding of prompt engineering, embeddings, vector databases (Pinecone, Weaviate, FAISS, pgvector), and RAG pipelines.

- Strong background in cloud platforms (AWS, GCP, Azure), containerization, and orchestration.

- Deep understanding of REST/GraphQL APIs, async programming, task queues, and caching mechanisms.

- Familiarity with SQL/NoSQL databases (PostgreSQL, MongoDB, Redis).

- Experience using AI-assisted tools such as GitHub Copilot, ChatGPT API, AutoGen, or OpenDevin for coding and testing automation.

- Exposure to CI/CD pipelines and Infrastructure as Code (Terraform, Pulumi).

- Knowledge of data preprocessing, NLP/NLU, and model evaluation techniques.

- Data Engineering & Processing, Data pipeline development , ETL/ELT processes, Batch processing and stream processing frameworks, Large-scale data handling with pandas, NumPy, Dask