Top 4 Mistral ChatGPT Prompt Engineering Jobs

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- $350,000 - $475,000 a year
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- Computer Science
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- Dental insurance
Data Scientist - AI Engineer
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6d
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- Full-time
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- Senior level
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AI ML Engineer (7+ Years)

InApp -
Remote

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Job Details

Qualifications

AI models
Containerization systems
Systems integration
Data Integration (Data management)
Continuous Delivery (CD) implementation
Text-to-speech
Tooling
Infrastructure as Code (IaC)
Testing and evaluation
Generative models
AI platforms (beyond public GPTs)
Computational framework
Prompt engineering
Microservices
AWS
Model deployment
Developing large-scale AI models
Natural language processing
Developing automated testing protocols
Developing and maintaining backend systems
Cloud automation
DevOps automation
Machine learning libraries
Data analytics tools
Real-time data processing implementation
Batch data processing
Machine learning frameworks
MLOps
Generative AI

Full Job Description

Experience : Experience: 7+ years in Python development with proven experience in AI and productivity tooling Project Location(s) : Trivandrum / Kochi / Remote

Notice Period: Immediate joiners
Number of Openings: 1
Working hours : 3 PM – 12 AM 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

Preferred Skills

Hands-on with multi-modal AI (vision, text-to-speech, speech-to-text).
Experience with MLOps practices including CI/CD for AI pipelines, model monitoring, and drift detection.
Background in fine-tuning, reinforcement learning, or custom model training.
Familiarity with enterprise security standards (GDPR, HIPAA, SOC2).
Contributions to open-source or personal AI-assisted coding initiatives.

Soft Skills

Excellent problem-solving and analytical thinking ability.
Strong cross-functional collaboration and communication skills.
Clear technical documentation habits.

Apply Now

Refine Your Search

Mistral ChatGPT Prompt Engineering jobs

AI ML Engineer (7+ Years)

Software Engineer, Security

Data Scientist - AI Engineer

ChatGPT Alternatives for Engineering Teams: Why Tooling is Secondary to Execution

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