Verified Job
IT / Software / Data Analyst
Software Engineer, AI-Powered Advertising Agents 1-2 year Experiece
IT / Software / Data Analyst
#731385
Remote / WFH
Prometteur Solutions Pvt. Ltd.
Job Description
We at Prometteur Solutions Pvt. Ltd. are a team of IT experts, who came with a promise of delivering technology-empowered business solutions. We provide world-class software and web development services that focus on playing a supportive role to your business and its holistic growth. Our highly-skilled associates and global delivery capabilities ensure the accessibility and scale to align client's technology solutions with their business needs. Our offerings span the entire IT lifecycle: from Consulting through Packaged, Custom, and Cloud Applications as well as a variety of Infrastructure Services.
Job Description
Provide technical leadership and mentorship to engineering teams while collaborating with architects, product managers, and UX designers to create innovative AI solutions that address complex customer challenges.
▪ Lead the design, development, and deployment of AI-driven features. Drive end-to-end ownership— from feasibility analysis and design specifications to execution and release—while ensuring quick iterations based on customer feedback in a fast-paced Agile environment.
▪ Spearhead technical design meetings and produce detailed design documents that outline scalable, secure, and robust AI architectures.
▪ Ensure that the solutions are aligned with long-term product strategy and technical roadmaps.
▪ Implement and optimize LLMs for specific use cases, including fine-tuning models, deploying pre trained models, and evaluating their performance.
▪ Develop AI agents powered by RAG systems, integrating external knowledge sources to improve the accuracy and relevance of generated content.
▪ Design, implement, and optimize vector databases (e.g., FAISS, Pinecone, Weaviate) for efficient and scalable vector search, and work on various vector indexing algorithms.
▪ Create sophisticated prompts and fine-tune them to improve the performance of LLMs in generating precise and contextually relevant responses.
▪ Utilize evaluation frameworks and metrics (e.g., Evals) to assess and improve the performance of generative models and AI systems.
▪ Work with data scientists, engineers, and product teams to integrate AI-driven capabilities into customer-facing products and internal tools.
▪ Stay up to date with the latest research and trends in LLMs, RAG, and generative AI technologies to drive innovation in the company’s offerings.
▪ Continuously monitor and optimize models to improve their performance, scalability, and cost efficiency.
We'd Love for You to Have: ▪ 1 to 2 years of experience and strong understanding of LLMs and their underlying principles — transformer architecture, attention mechanisms, and hyperparameter tuning.
▪ Proven experience designing and building AI agents, including multi-agent orchestration, tool-use patterns, multi-step planning, and agent memory architectures (short-term and long-term).
▪ Hands-on experience with agentic frameworks such as LangGraph, CrewAI, or AutoGen, and familiarity with RAG pipelines that integrate external knowledge sources (documents, databases, APIs).
▪ In-depth knowledge of vector databases and indexing algorithms; practical experience with FAISS, Pinecone, Weaviate, or Milvus.
▪ Experience with agent observability, tracing, and guardrails — tools like Langfuse or equivalent — to ensure reliability, safety, and debuggability of agentic systems.
▪ Proficiency in prompt engineering — crafting, iterating, and optimizing complex prompts for context sensitive, domain-specific LLM outputs.
▪ Familiarity with Evals and other performance evaluation tools for measuring model quality, relevance, and efficiency.
▪ Proficiency in Python and experience with machine learning libraries such as TensorFlow, PyTorch, and Hugging Face Transformers.
▪ Experience with data preprocessing, vectorization, and handling large-scale datasets.
▪ Ability to present complex technical ideas and results to both technical and non-technical stakeholders.
Qualifications
Experience in building AI agents using graph-based architectures, including knowledge graph embeddings and graph neural networks (GNNs).
▪ Experience with training small base models using custom data, including data collection, pre processing, and fine-tuning models to specific domains or tasks.
▪ Familiarity with deploying AI models on cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).
▪ Familiarity with programmatic advertising, RTB, or ad auction mechanics.
▪ Knowledge of MCP (Model Context Protocol) or similar tool-integration standards
▪ Publication or contributions to research in AI, LLMs, or related fields.
Job Description
Provide technical leadership and mentorship to engineering teams while collaborating with architects, product managers, and UX designers to create innovative AI solutions that address complex customer challenges.
▪ Lead the design, development, and deployment of AI-driven features. Drive end-to-end ownership— from feasibility analysis and design specifications to execution and release—while ensuring quick iterations based on customer feedback in a fast-paced Agile environment.
▪ Spearhead technical design meetings and produce detailed design documents that outline scalable, secure, and robust AI architectures.
▪ Ensure that the solutions are aligned with long-term product strategy and technical roadmaps.
▪ Implement and optimize LLMs for specific use cases, including fine-tuning models, deploying pre trained models, and evaluating their performance.
▪ Develop AI agents powered by RAG systems, integrating external knowledge sources to improve the accuracy and relevance of generated content.
▪ Design, implement, and optimize vector databases (e.g., FAISS, Pinecone, Weaviate) for efficient and scalable vector search, and work on various vector indexing algorithms.
▪ Create sophisticated prompts and fine-tune them to improve the performance of LLMs in generating precise and contextually relevant responses.
▪ Utilize evaluation frameworks and metrics (e.g., Evals) to assess and improve the performance of generative models and AI systems.
▪ Work with data scientists, engineers, and product teams to integrate AI-driven capabilities into customer-facing products and internal tools.
▪ Stay up to date with the latest research and trends in LLMs, RAG, and generative AI technologies to drive innovation in the company’s offerings.
▪ Continuously monitor and optimize models to improve their performance, scalability, and cost efficiency.
We'd Love for You to Have: ▪ 1 to 2 years of experience and strong understanding of LLMs and their underlying principles — transformer architecture, attention mechanisms, and hyperparameter tuning.
▪ Proven experience designing and building AI agents, including multi-agent orchestration, tool-use patterns, multi-step planning, and agent memory architectures (short-term and long-term).
▪ Hands-on experience with agentic frameworks such as LangGraph, CrewAI, or AutoGen, and familiarity with RAG pipelines that integrate external knowledge sources (documents, databases, APIs).
▪ In-depth knowledge of vector databases and indexing algorithms; practical experience with FAISS, Pinecone, Weaviate, or Milvus.
▪ Experience with agent observability, tracing, and guardrails — tools like Langfuse or equivalent — to ensure reliability, safety, and debuggability of agentic systems.
▪ Proficiency in prompt engineering — crafting, iterating, and optimizing complex prompts for context sensitive, domain-specific LLM outputs.
▪ Familiarity with Evals and other performance evaluation tools for measuring model quality, relevance, and efficiency.
▪ Proficiency in Python and experience with machine learning libraries such as TensorFlow, PyTorch, and Hugging Face Transformers.
▪ Experience with data preprocessing, vectorization, and handling large-scale datasets.
▪ Ability to present complex technical ideas and results to both technical and non-technical stakeholders.
Qualifications
Experience in building AI agents using graph-based architectures, including knowledge graph embeddings and graph neural networks (GNNs).
▪ Experience with training small base models using custom data, including data collection, pre processing, and fine-tuning models to specific domains or tasks.
▪ Familiarity with deploying AI models on cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).
▪ Familiarity with programmatic advertising, RTB, or ad auction mechanics.
▪ Knowledge of MCP (Model Context Protocol) or similar tool-integration standards
▪ Publication or contributions to research in AI, LLMs, or related fields.
