llm engineer

Oracle

<div>We are looking for a senior engineer who specializes in LLM systems, prompt engineering, and agentic application deployment, combined with strong MLOps and cloud platform engineering experience.
You will design, deploy, and scale Generative AI models, retrieval-augmented generation (RAG) pipelines, and autonomous agent frameworks on OCI.
In this role, you&rsquo;ll work closely with data scientists, platform architects, and research teams to build production-grade AI systems, including:
<ul><li>LLM finetuning and adaptation
</li><li>Prompt and prompt-chain optimization
</li><li>Multi-agent orchestration frameworks
</li><li>Automated evaluation and guardrail systems
</li><li>Model + data drift monitoring and continuous retraining workflows
</li></ul>You will be a key contributor to defining our AI platform architecture, ensuring operational scale, efficiency, security, and reliability.</div>
 <div>Your responsibilities will include:
LLM &amp; Agentic Development:
<ul><li>Design, evaluate, and optimize prompts, prompt chains, and agent behaviors.
</li><li>Build and deploy RAG systems, vector search pipelines, and knowledge-grounding layers.
</li><li>Develop agent orchestration workflows using frameworks like LangChain, LlamaIndex, Guidance, or AG2.
</li><li>Integrate LLMs with external tools, APIs, and internal business systems.
</li></ul>LLMOps &amp; Platform Engineering:
<ul><li>Deploy and host open-source and proprietary LLMs on OCI (e.g., GPT, Llama, Mistral, Grok).
</li><li>Implement automated evaluation frameworks to measure truthfulness, relevance, safety, latency, and cost.
</li><li>Manage fine-tuning, LoRA adaptation, or embedding model selection.
</li></ul>Data Pipeline &amp; Quality:
<ul><li>Build pipelines that ensure data freshness, traceability, and semantic relevance for downstream LLM tasks.
</li><li>Use data validation frameworks (e.g., Great Expectations, Evidently) to detect drift or knowledge degradation.
</li></ul>Observability, Monitoring &amp; Cost Optimization:
<ul><li>Track LLM system performance, token usage, latency, and operational anomalies.
</li><li>Implement model guardrails, safety layers, and automated fallback behavior.
</li></ul>Collaboration &amp; Mentorship
<ul><li>Work directly with Data Science + Product to translate domain problems into LLM+Agent architectures.
</li><li>Mentor engineers and scientists on LLM deployment, prompt strategy, and evaluation methods.
</li><li>Work closely with architects, product teams, data engineers, and other stakeholders to deliver end-to-end AI solutions that address business needs.
</li></ul>Technical Skills:
<ul><li>Strong Python engineering background.
</li><li>Experience with LLMs, RAG pipelines, or agent frameworks (LangChain, LlamaIndex, Haystack, AG2, etc.).
</li><li>Hands-on cloud infrastructure experience (OCI, AWS, GCP, or Azure).
</li><li>Experience with vector databases (e.g., Chroma, Pinecone, Weaviate, Milvus, PGVector).
</li><li>Experience with Kubernetes, Docker, and CI/CD automation.
</li></ul>Nice to Have:
<ul><li>Experience fine-tuning or adapting LLMs (e.g., LoRA, QLoRA, RLHF, supervised finetuning).
</li><li>Prompt evaluation and automated testing frameworks (e.g., RAGAS, TruLens, DeepEval).
</li><li>Experience deploying microservices architectures in production environments.
</li></ul>Qualifications :
<ul><li>8+ years of experience in software engineering, machine learning engineering, or platform engineering, with at least 2+ years focused on ML/AI systems in production.
</li><li>Hands-on experience developing or deploying Large Language Model (LLM) systems, including prompt engineering, RAG pipelines, agent-based workflows, or LLM fine-tuning.
</li><li>Strong proficiency in Python and experience with one or more LLM/agent frameworks (e.g., LangChain, LlamaIndex, Haystack, Guidance, AG2).
</li><li>Experience designing and operating cloud-native ML systems on OCI, AWS, GCP, or Azure.
</li><li>Proficiency with Kubernetes, Docker, and CI/CD pipelines for deploying and scaling services.
</li><li>Experience with data workflow orchestration (e.g., Airflow, Prefect, Dagster) and data validation frameworks (e.g., Great Expectations, Evidently).
</li><li>Strong understanding of vector databases (e.g., Pinecone, Weaviate, Milvus, Chroma, Postgres + pgvector).
</li><li>Demonstrated ability to build and maintain production monitoring, alerting, and observability dashboards (e.g., Prometheus, Grafana).
</li><li>Excellent communication and collaboration skills with the ability to mentor and lead technical discussions.
</li><li>Bachelor&rsquo;s or master&rsquo;s degree in computer science, engineering, or a related field, or equivalent practical experience.</li></ul></div>

Principal LLM & MLOps Engineer

Apple

<div>We work on Apple scale opportunities and challenges. We are engineers at heart. We like solving technical problems. We believe a good engineer has the curiosity to dig into inner workings of technology and is always experimenting, reading and in constant learning mode. If you are a software engineer with passion to code and dig deeper into any technology, love knowing the internals, fascinated by distributed systems architecture, we want to hear from you. 
 
 
 
Description 
We are seeking a highly skilled LLM Ops and ML Ops Engineer to lead the deployment, scaling, monitoring, and optimization of large language models (LLMs) across diverse environments. This role is critical to ensuring our machine learning systems are production-ready, high-performing, and resilient. The ideal candidate will have deep expertise in Python programming / Go Programming, a comprehensive understanding of LLM internals, and hands-on experience with various inference engines and deployment strategies. The person should be capable of exhibiting deftness to balance multiple simultaneous competing priorities and deliver solutions in a timely manner. The person should be able to understand complex architectures and be comfortable working with multiple teams 
 
KEY RESPONSIBILITIES: 
<ul><li>Design and build scalable infrastructure for fine-tuning, and deploying large language models.</li></ul> 
<ul><li>Develop and optimize inference pipelines using popular frameworks and engines (e.g. TensorRT, vLLM, Triton Inference Server).</li></ul> 
<ul><li>Implement observability solutions for model performance, latency, throughput, GPU/TPU utilization, and memory efficiency.</li></ul> 
<ul><li>Own the end-to-end lifecycle of LLMs in production-from experimentation to continuous integration and continuous deployment (CI/CD).</li></ul> 
<ul><li>Collaborate with research scientists, ML engineers, and backend teams to operationalize groundbreaking LLM architectures.</li></ul> 
<ul><li>Automate and harden model deployment workflows using Python, Kubernetes, Containers and orchestration tools like Argo Workflows and GitOps.</li></ul> 
<ul><li>Design reproducible model packaging, versioning, and rollback strategies for large-scale serving.</li></ul> 
<ul><li>Stay current with advances in LLM inference acceleration, quantization, distillation, and model compilation techniques (e.g., GGUF, AWQ, FP8).</li></ul> 

Preferred Qualifications 
Experience integrating LLMs into micro-services or edge inference platforms. 
 
Experience with Ray distributed inference
 
 
Hands-on with quantization libraries 
 
Contributions to open-source ML infrastructure or LLM optimization tools. 
 
Familiarity with cloud platforms (AWS, GCP) and infrastructure-as-code (Terraform). 
 
Exposure to secure and compliant model deployment workflows 
 
 
 
Minimum Qualifications 
5+ years of experience in LLM/ML Ops, DevOps, or infrastructure engineering with a focus on machine learning systems. 
 
Advance level proficiency in Python/Go, with ability to write clean, performant, and maintainable production code. 
 
Deep understanding of transformer architectures, LLM tokenization, attention mechanisms, memory management, and batching strategies. 
 
Proven experience deploying and optimizing LLMs using multiple inference engines. 
 
Strong background in containerization and orchestration (Kubernetes, Helm). 
 
Familiarity with monitoring tools (e.g., Prometheus, Grafana), logging frameworks, and performance profiling. 
 
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LLM Ops Engineer

Application House Limited

Job Title:Machine Learning Engineer (Inference &amp; Systems)(also known as Inference Engineer)Location:Work from Home (fully flexible Remote)Job Timing:Part-Time, flexible any time that can be optionally transform to full-timeAbout the Role:We&rsquo;re building a next-generation cloud platform to serve multimodal AI, LLMs, vision, audio and other machine learning models at scale. As a Machine Learning Engineer (Inference &amp; Systems), you&rsquo;ll design and optimize runtime systems, OpenAI-compatible APIs, and distributed GPU pipelines for fast, cost-efficient inference and fine-tuning.You&rsquo;ll work with frameworks like vLLM, TensorRT-LLM, and TGI to design, optimize, and deploy distributed inference engines that serve text, vision, and multimodal models with low latency and high throughput. This includes deploying models such as LLaMA 3, Mistral, diffusion, ASR, TTS, and embeddings, while focusing on GPU/accelerator optimizations, software&ndash;hardware co-design, and fault-tolerant large-scale systems that power real-world applications and developer tools.You&rsquo;ll work at the intersection of machine learning, cloud infrastructure, and systems engineering, focusing on high-throughput, low-latency inference and cost-efficient deployment. This role offers a unique opportunity to shape the future of AI inference infrastructure, from cutting-edge model serving systems to production-grade deployment pipelines.If you're passionate about pushing the boundaries of AI inference, we&rsquo;d love to hear from you!Key Responsibilities:&middot; Deploy and maintain LLMs (e.g., LLaMA 3, Mistral) and ML models using serving engines such as vLLM, Hugging Face TGI, TensorRT-LLM, or FasterTransformer.&middot; Design and develop fault-tolerant, high-concurrency, large-scale distributed inference engines for text, image, LLMs and multimodal models that are fault-tolerant, high-performance, and cost-efficient.&middot; Implement, optimize distributed inference and parallelism strategies: Mixture of Experts (MoE), tensor parallelism, pipeline parallelism, tensor parallelism, pipeline parallelism for high-performance serving.&middot; Integrate vLLM, TGI, SGLang, FasterTransformer, and explore emerging inference frameworks.&middot; Build and scale an OpenAI-compatible API layer to expose models for customer use.&middot; Experiment with model quantization, caching, and parallelism to reduce inference costs.&middot; Optimize GPU usage, memory, and batching to achieve low-latency, high-throughput inference.&middot; Optimize GPU performance using CUDA graph optimizations, TensorRT-LLM, Triton kernels, PyTorch compilation (torch.compile), quantization, speculative decoding to maximize efficiency.&middot; Work with cloud GPU providers (RunPod, Vast.ai, AWS, GCP, Azure) to manage costs and availability.&middot; Develop runtime inference services and APIs for LLMs, multimodal models, and fine-tuning pipelines.&middot; Build monitoring and observability for inference services to integrate inference metrics (latency, throughput, GPU utilization) into monitoring dashboards (Grafana, Prometheus, Loki, OpenTelemetry).&middot; Collaborate with backend and DevOps engineers to ensure secure, reliable APIs with rate-limiting and billing hooks.&middot; Document deployment processes and provide guidance to other engineers using the platform.Requirements:&middot; Experience: 3+ years in deep learning inference, fault-tolerant, distributed systems, or high-performance computing.&middot; Proven experience in deploying ML/LLM models to production.&middot; Inference: Hands-on experience with at least one inference engine: vLLM, TGI, SGLang, TensorRT-LLM, FasterTransformer, or Triton.&middot; Runtime Services: Prior work implementing large-scale inference or serving pipelines.&middot; Solid understanding of GPU memory management, batching, and distributed inference. Strong knowledge of GPU programming (CUDA, Triton, TensorRT), compiler, model quantization, and GPU cluster scheduling.&middot; Experienced in the GPU/ML stack including PyTorch, Hugging Face Transformers, and GPU-accelerated inference.&middot; Deep understanding of Transformer architectures, LLM/VLM/Diffusion model optimization, and KV cache systems like Mooncake, PagedAttention, or custom in-house variants solutions, with experience enhancing them for long-context serving and applying inference optimization techniques such as workload scheduling, efficient kernels, and CUDA graphs.&middot; Comfortable working with cloud GPU platforms (AWS/GCP/Azure) or GPU marketplaces (RunPod, Vast.ai, TensorDock) to profile bottlenecks and optimize GPU utilization.&middot; Benchmark and tune multi-GPU clusters for throughput and memory efficiency.&middot; Experience building REST APIs or gRPC services (FastAPI, Flask, or similar).&middot; Programming: Proficient in Python, Go, Rust, C++, CUDA for high-performance systems.&middot; Systems knowledge: Demonstrated experience with distributed systems (storage, search, compute, or inference). Strong understanding of multi-threading, memory management, networking, storage, performance tuning.&middot; Familiarity with containerization (Docker) and orchestration (Kubernetes).&middot; Strong problem-solving and debugging skills across ML + infra stack.&middot; Familiarity with distributed storage (Ceph, HDFS, 3FS).&middot; Knowledge of datacenter networking (RDMA, RoCE).Nice to Have:&middot; Experience with Stripe or other billing systems for metered API usage.&middot; Experience with large-scale datacenter networking (RDMA/RoCE).&middot; Familiarity with distributed storage (Ceph, HDFS, 3FS).&middot; Knowledge of Redis or Envoy for request rate limiting.&middot; Familiarity with observability tools (Grafana, Prometheus, Loki).&middot; Exposure to MLOps pipelines (CI/CD with Azure DevOps or GitHub Actions).&middot; Exposure to observability stacks (Prometheus, Grafana, Loki).&middot; Experience with model fine-tuning pipelines and GPU scheduling.&middot; Understanding of rate limiting, quota enforcement, and billing hooks in ML APIs.&middot; Prior work at an AI infra company (Together.ai, Modal, Anyscale, Replicate, etc.)Why Join Us?&middot; Work from Anywhere &ndash; 100% remote, with the freedom to work from anywhere in the USA.&middot; Fully Flexible Shifts &ndash; complete control over your working hours; results matter more than clocking in.&middot; Career Growth &amp; Fast-Track Promotions &ndash; we guarantee quickest promotion opportunities and clear pathways for advancement.&middot; Professional Development &ndash; training budget, mentorship, and exposure to cutting-edge Salesforce, AI/ML, and cloud technologies.&middot; Global Collaboration &ndash; work with an international, diverse, and inclusive team.&middot; Innovative Environment &ndash; freedom to experiment with new tools, frameworks, and ideas.&middot; Accelerated Salary Growth + Performance Incentives &ndash; ambitious and hard-working team members are rewarded with fast upward salary progression alongside strong performance bonuses.Job Type: Part-timeBenefits:<ul><li>Flexible schedule</li></ul>Work Location: Remote

LLM Inference Engineer

iOPTIME Pvt. Limited

We are looking for a passionate AI Engineer with hands-on experience in Generative AI, RAG systems, LLMs, and Computer Vision to help design and deploy next-generation AI solutions. You will work on building intelligent systems using LangChain, LLM fine-tuning, OCR, and LLM evaluation pipelinesKey Responsibilities:<ul><li>Develop and optimize RAG-based architectures for high-quality contextual retrieval and response generation.</li><li>Work on LLM fine-tuning, prompt engineering, and evaluation frameworks to measure model performance and accuracy.</li><li>Fine-tune LLM models to cater to specific business needs, ensuring high accuracy and efficiency.</li><li>Apply Computer Vision techniques for image processing, object detection, and real-time video analytics.</li><li>Collaborate with backend and ML teams to integrate AI components into production using FastAPI, Docker, and GCP or AWS.</li><li>Continuously explore advancements in GenAI, LangChain, and LLM evaluation to enhance system performance.</li></ul>Required Skills &amp; Qualifications:<ul><li>Bachelor&rsquo;s or Master degree in Computer Science, AI/ML, or a related field.</li><li>2 years of hands-on experience in AI/ML model development.</li><li>Strong programming skills in Python with frameworks such as PyTorch or TensorFlow.</li><li>Experience with LLMs, LangChain, RAG, or LLM evaluation pipelines.</li><li>Knowledge of OCR tools (e.g., Tesseract, EasyOCR) and Computer Vision frameworks (e.g., OpenCV, YOLO).</li><li>Familiarity with model deployment, vector databases, and inference optimization.</li><li>Good understanding of prompt engineering, fine-tuning, and model evaluation metrics.</li><li>Strong analytical mindset, problem-solving ability, and teamwork skills.</li></ul>Job Types: Full-time, Contract Contract length: 12 monthsPay: Rs100,000.00 per monthAbility to commute/relocate:<ul><li>Islamabad G-8/Markaz: Reliably commute or planning to relocate before starting work (Required)</li></ul>Application Question(s):<ul><li>What is your expected salary?</li><li>It's an onsite full-time job, are you comfortable commuting to this job's location?</li></ul>Education:<ul><li>Bachelor's (Required)</li></ul>Experience:<ul><li>Generative AI: 2 years (Required)</li></ul>Work Location: In person

AI Engineer

BOOM

Job Title: AI Engineer (GenAI / NLP / LLM + Full Stack Developer)Location: Mumbai (On-site only)** Job Type: Full-time** Preference: Immediate joiners Note: A portfolio showcasing past projects is mandatory.About the RoleWe are an innovation-led digital solutions company seeking a talented AI Engineer with strong Full Stack Web Development skills. You will build advanced GenAI workflows, scalable backend services, and modern web interfaces. This role is perfect for someone who enjoys building real-world AI applications end-to-end&mdash;from UI to agents, APIs, vector DBs, and cloud deployments.Key ResponsibilitiesGenAI / NLP / LLM Engineering<ul><li>Build and maintain complex agent workflows using LangGraph</li><li>Work with LangChain (agents, retrievers, tools, pipelines)</li><li>Implement RAG systems (embeddings, retrieval, ranking, context building)</li><li>Integrate LLM APIs: OpenAI, Gemini, LLaMA, HuggingFace</li><li>Apply LLMOps practices (evaluation, monitoring, versioning, logging)</li><li>Work with vector databases: Pinecone, Chroma, Weaviate</li></ul>Backend &amp; API Development<ul><li>Build scalable microservices using FastAPI</li><li>Develop REST APIs and integrate external APIs (Google APIs, SERP, etc.)</li><li>Containerize services using Docker</li><li>Work with microservice architectures and asynchronous processing</li></ul>Full Stack Web Development<ul><li>Build and maintain web applications using:</li></ul>Python, JavaScript, HTML, CSS, MySQL/MongoDB<ul><li>Ability to design and implement frontend components</li><li>Work with JS frameworks or libraries (React/jQuery)</li><li>Build responsive UI/UX with Figma/Adobe XD</li><li>Handle onsite debugging, performance improvements, and integrations</li><li>WordPress theme/plugin customizations (good to have)</li></ul>Automation &amp; Data Pipelines<ul><li>Web scraping using BS4, Playwright, Selenium, Requests</li><li>Integrate scraped data into LLM/RAG pipelines</li><li>Build automated ingestion and processing workflows</li></ul>AI Tools &amp; APIs<ul><li>Hands-on with Whisper, OCR APIs, Vision models, SERP APIs</li><li>Build AI workflows connecting APIs, vector DBs, microservices</li></ul>Cloud &amp; DevOps<ul><li>Deploy on AWS/GCP (Lambda, S3, GCE, serverless functions)</li><li>Docker-based deployments and environment management</li><li>Basic understanding of CI/CD pipelines</li></ul>Required Skills &amp; Experience<ul><li>Bachelor&rsquo;s degree in Computer Science/IT or equivalent</li><li>Minimum 3 years of experience in AI engineering, backend systems, or full-stack development</li><li>Strong Python skills + good JS knowledge</li><li>Experience deploying real-world AI/ML/LLM applications</li><li>Excellent problem-solving, debugging, and communication skills</li><li>Portfolio of past work is mandatory (GitHub, website, code samples, or project showcase)</li></ul>Bonus Skills (Nice to Have)<ul><li>Vue.js/Angular</li><li>Knowledge of Elasticsearch/Algolia</li><li>Experience with Firebase</li><li>Understanding of SEO and web performance</li><li>Experience building voice-based or interactive AI tools</li></ul>Perks &amp; Benefits<ul><li>Health Insurance</li><li>Provident Fund (PF)</li><li>Work on innovative AI-first projects</li><li>Supportive environment with learning &amp; upskilling opportunities</li><li>Strong career growth in an AI-driven organisation</li></ul>How to ApplySubmit your resume + portfolio link and mention your earliest joining date. We look forward to meeting builders who love pushing the boundaries of AI!Job Type: Full-timePay: &#8377;40,000.00 - &#8377;60,000.00 per monthBenefits:<ul><li>Health insurance</li><li>Provident Fund</li></ul>Education:<ul><li>Bachelor's (Required)</li></ul>Experience:<ul><li>total: 2 years (Required)</li></ul>Work Location: In person

RingCentral

<div>
 
 
 Job Description:
 
 
 
 We are seeking an experienced AI Engineer with a strong background in Natural Language Understanding (NLU) who is passionate about pushing the boundaries of Conversational AI. In this role, you will design, develop, and deploy scalable AI solutions leveraging LLMs, Retrieval-Augmented Generation (RAG), and prompt engineering techniques to power intelligent products and services.
 
 
 As part of our ML/AI team, you&rsquo;ll own the full lifecycle of model development &mdash; from data preparation and fine-tuning to inference optimization and deployment in production environments.
 
 
 
 
 
 
 Responsibilities:
 
 
 <ul>
 <li>
 Design, fine-tune, and deploy LLM-based applications for Conversational AI use cases
 </li>
 <li>
 Build scalable retrieval-augmented generation (RAG) pipelines that combine LLMs with structured/unstructured data sources
 </li>
 <li>
 Develop prompt engineering strategies, templates, and evaluation frameworks for LLM-driven workflows
 </li>
 <li>
 Collaborate with cross-functional teams to identify and implement AI-driven solutions to business problems
 </li>
 <li>
 Optimize models for low-latency inference using quantization, distillation, and other model optimization techniques (e.g., ONNX, TensorRT)
 </li>
 <li>
 Build robust data processing, labeling, and augmentation pipelines to improve model performance
 </li>
 <li>
 Implement monitoring and evaluation systems for deployed LLMs, ensuring reliability, fairness, and safety
 </li>
 <li>
 Stay current with emerging trends in LLMs, retrieval systems, and generative AI frameworks
 </li>
 </ul>
 
 
 
 
 
 Requirements:
 
 
 <ul>
 <li>
 1-4 years of hands-on experience in NLP/LLM.
 </li>
 <li>
 Strong proficiency in Python and PyTorch and related frameworks (like Hugging Face Transformers, Sentence Transformers etc.)
 </li>
 <li>
 Proven experience developing and deploying NLP or LLM pipelines in production environments at scale
 </li>
 <li>
 Solid understanding of transformer architectures and attention mechanisms
 </li>
 <li>
 Proficiency in using LLM provider APIs such as OpenAI, Gemini etc.including prompt design, fine-tuning, and evaluation
 </li>
 <li>
 Experience with model optimization techniques such as quantization, pruning, ONNX, TensorRT, or model distillation
 </li>
 <li>
 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Artificial Intelligence, or a related field
 </li>
 </ul>
 
 
 
 
 
 Nice to Have:
 
 
 <ul>
 <li>
 Hands-on experience with RAG and vector databases (e.g., FAISS, Qdrant, pgVector etc. )
 </li>
 <li>
 Prior work on LLM fine-tuning, alignment, or evaluation
 </li>
 <li>
 Experience with LLM orchestration frameworks such as LlamaIndex or similar tools
 </li>
 <li>
 Familiarity with multi-provider LLM orchestration, integrating APIs from OpenAI, Gemini etc. and others for fallbacks, routing, or ensemble strategies
 </li>
 <li>
 Knowledge of MLOps for LLMs, including model serving and monitoring
 </li>
 <li>
 Understanding of embedding models, context management, and token optimization for scalable LLM applications
 </li>
 </ul>
 
 
</div>

AI/ML Engineer

IKONIC

<div>
 
 Position Title:
 
 Generative AI Engineer
 
 
 
 Timings:
 
 4:00pm - 1:00am
 
 
 
 Department:
 
 AI ML
 
 
 
 Location:
 
 Plot # 176, Korang Road, near Rahat Bakers, I-10/3, Islamabad
 
 
 
 About Us:
 
 
 
 IKONIC is a US-based IT company headquartered in Miami, Florida, serving clients globally for over 9 years. Were known for building scalable digital solutions and high-performing product teams. Innovation, ownership, and outcomes are core to how we work.
 
 
 
 About The Role
 
 
 
 Were looking for a
 
 Generative AI Engineer
 
 with deep expertise in Agentic AI systems, RAG architectures, and
 
 LangChain/LangGraph
 
 frameworks to build next-generation AI applications.
 
 
 This role is ideal for someone passionate about LLMs, prompt engineering, fine-tuning, AI agents, and exploring how generative models can power the next wave of automation and creativity.
 
 
 
 Education &amp; Professional Qualification:
 
 
 
 <ul>
 <li>
 Bachelor or equivalent degree from a reputed educational institute, preferably in computer programming, computer science, or a related field.
 </li>
 <li>
 Certification(s): Not mandatory, but certification in a relevant field is a plus.
 
 
 
 </li>
 </ul>
 
 Experience:
 
 
 
 Minimum 3 years of experience in AI/ML engineering with a proven portfolio of relevant projects.
 
 
 
 Experience:
 
 
 
 Minimum 3 years of experience in AI/ML engineering with a proven portfolio of relevant projects.
 
 
 
 Key Responsibilities:
 
 
 
 <ul>
 <li>
 Design and build Agentic AI architectures using LangChain, LangGraph, or similar frameworks.
 </li>
 <li>
 Implement Retrieval-Augmented Generation (RAG) systems for knowledge retrieval and contextualized responses.
 </li>
 <li>
 Design, develop, and deploy Generative AI solutions leveraging state-of-the-art LLMs (GPT, Claude, LLaMA, Gemini, etc.).
 </li>
 <li>
 Build AI chatbots and virtual assistants with advanced memory, reasoning, and multi-turn conversation abilities.
 </li>
 <li>
 Conduct continuous performance tuning and evaluation to enhance chatbot accuracy, tone, and user experience.
 </li>
 <li>
 Develop AI workflows that involve tool use, memory management, and multi-agent collaboration.
 </li>
 <li>
 Deep understanding of multi-layer neural network models and architectures such as CNN, RNN, LSTM, encoders, transformers etc.
 </li>
 <li>
 Integrate vector databases (Pinecone, FAISS, Weaviate, Chroma) for semantic search and long-term memory.
 </li>
 <li>
 Connect LLMs (OpenAI, Anthropic, Mistral, Llama) with APIs, databases, and real-world tools.
 </li>
 <li>
 Fine-tune and evaluate models for accuracy, latency, and reasoning depth.
 </li>
 <li>
 Collaborate with backend and data teams to deploy AI pipelines into scalable production environments.
 </li>
 <li>
 Stay updated with the latest in Agentic AI research, LLM orchestration, and autonomous systems.
 
 
 
 </li>
 </ul>
 
 Requirements:
 
 
 
 <ul>
 <li>
 Bachelors or Masters degree in Computer Science, AI, Data Science, or related field.
 </li>
 <li>
 3+ years of experience in AI / ML / LLM-based development.
 </li>
 <li>
 Strong proficiency in Python and experience with LangChain, LangGraph, or LlamaIndex.
 </li>
 <li>
 Proven experience building chatbots or conversational AI applications using LLMs.
 </li>
 <li>
 Hands-on knowledge of RAG pipelines, document embedding, and vector search.
 </li>
 <li>
 Familiarity with LLM APIs (OpenAI, Anthropic, Mistral, Hugging Face, Gemini).
 </li>
 <li>
 Understanding of prompt engineering, tool calling, function calling, and memory management.
 </li>
 <li>
 Experience integrating chatbots with web applications, APIs, or CRM systems.
 </li>
 <li>
 Working knowledge of Docker, REST APIs, and cloud services (AWS/GCP/Azure).
 
 
 
 </li>
 </ul>
 
 Benefits:
 
 
 
 <ul>
 <li>
 EOBI
 </li>
 <li>
 Provident Fund
 </li>
 <li>
 Medical Health Insurance
 </li>
 <li>
 Annual Leaves
 </li>
 <li>
 Casual leaves
 </li>
 <li>
 Sick Leaves
 </li>
 <li>
 Bereavement Leaves
 </li>
 <li>
 Marriage Leaves
 </li>
 <li>
 Paternity Leaves
 </li>
 <li>
 Maternity Leaves
 </li>
 <li>
 Recreational area for in-house games
 
 
 
 </li>
 </ul>
 
 Why Should You Join IKONIC?
 
 
 
 At IKONIC, we believe in providing development opportunities to all its employees, which is why our job comes with a variety of challenging assessments meant to catapult your career to the next level. This is made evident with our offerings such as:
 
 
 <ul>
 <li>
 A growth mindset through the help of experienced and helpful Mentors. Ikonic is made up of passionate individuals who aim to support each other in their training as well as day-to-day tasks.
 </li>
 <li>
 A Dynamic Environment where we focus on encouraging initiatives, promoting agility and creating a work/life balance. We know the value you bring in, and we aim to nurture it.
 </li>
 <li>
 Market Competitive Compensation based upon your professional qualifications and skill set.
 </li>
 <li>
 As an Equal employment opportunity provider. All employment associated decisions are based on an individual's merit.
 
 
 
 </li>
 </ul>
 
 Note:
 
 
 Above goals and job descriptions are indicative and subject to change.
 
</div>

Generative AI Engineer

Search 8856+ Llm engineer jobs near you this November. New roles added regularly across leading industries. Apply today.

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